An informative probability model enhancing real time
echobiometry to improve fetal weight estimation accuracy
G. Cevenini Æ F. M. Severi Æ C. Bocchi Æ
F. Petraglia Æ P. Barbini
Placental Elastography in Intrauterine Growth Restriction: A Case–control Studyasclepiuspdfs
Background: Intrauterine growth restriction (IUGR) is related to poor fetal outcome. Though, various tools are available for evaluation of IUGR they are notreliable inearly diagnosis of IUGR. Shear wave elastography (SWE) can be used to study the change in mechanical properties of various disease which can be a potential technique for early diagnosis of IUGR. Objective: The objective of the study was to compare the differences in SWE values of placentas between IUGR and normal pregnancies. Methodology: Normal second- and third-trimester pregnancies and IUGR pregnancies between 24 and 42 weeks period of gestation (POG), meeting the inclusion criteria were matched for age group and POG. SWE of placenta was performed in supine position during quiet respiration. The SWE of placenta was measured by placing the region of interest in relatively homogeneous area. The placental elasticity values obtained in pregnancies complicated by IUGR were compared with that of normal controls. Umbilical artery (UA) and fetal middle cerebral artery (MCA) Doppler findings were correlated with placental elasticity value of IUGR pregnancies.
Placental Elastography in Intrauterine Growth Restriction: A Case–control Studyasclepiuspdfs
Background: Intrauterine growth restriction (IUGR) is related to poor fetal outcome. Though, various tools are available for evaluation of IUGR they are notreliable inearly diagnosis of IUGR. Shear wave elastography (SWE) can be used to study the change in mechanical properties of various disease which can be a potential technique for early diagnosis of IUGR. Objective: The objective of the study was to compare the differences in SWE values of placentas between IUGR and normal pregnancies. Methodology: Normal second- and third-trimester pregnancies and IUGR pregnancies between 24 and 42 weeks period of gestation (POG), meeting the inclusion criteria were matched for age group and POG. SWE of placenta was performed in supine position during quiet respiration. The SWE of placenta was measured by placing the region of interest in relatively homogeneous area. The placental elasticity values obtained in pregnancies complicated by IUGR were compared with that of normal controls. Umbilical artery (UA) and fetal middle cerebral artery (MCA) Doppler findings were correlated with placental elasticity value of IUGR pregnancies.
GC–MS Analysis of Bioactive Compounds Present in Ethanol Extract of Combretum...ijtsrd
Phytoconstituents present in the ethanolic extract of Combretum hispidum leaves were explored by Gas Chromatography Mass Spectrometry analysis. The compounds were identified by the gas chromatography coupled with the mass spectrometry. The molecular weight and structure of the compounds of Combretum hispidum leaves were ascertained by interpretation of the spectrum of GC MS using the database of National Institute of Standard and Technology NIST . GC MS analysis of Combretum hispidum leaves revealed the presence of nineteen biological active compounds. The compounds are N Tosyl dl 3,4 dehydroprolylglycine, ethyl ester, 1 n Butoxy 2,2,3,3 tetramethylaziridine, 2 Butenoic acid, 3 methyl 4 tetrahydro 3,4 dihydroxy 5 3 2 hydroxy 1 methylpropyl oxiranyl methyl 2H pyran , Cobalt, octacarbonyl zinc di , 2Co Zn , 6 Dehydroxy 2,3,3,4,4,5,7 hepta O methylisoorientin, 2 naphthalenol, 3 5 3 nitrophenyl 1,3,4 oxadiazol 2 yl , L Proline, N 1 naphthoyl , dodecyl ester, 3,6 Dispirocyclohexyl 1,2,3,4,5,6,7,8 octahydro 1,8 acridinedione, Sarcosine, N 2 chloroethoxycarbonyl , heptadecyl ester, Cycloocta 1,2 b 4,3 b 5,6 b 8,7 b tetrakis 1 benzothiophene, Butanoic acid, 2 chloro 3 methyl , 4 5 heptyl 2 pyrimidinyl phenyl ester, Friedooleanan 1 one, 3,24 dihydroxy , 9 O Methyl 4,5 deoxymaytansino, Ditelluride, di 1 naphthalenyl, tert Butylstibinous acid thioanhydride, l Leucine, N methyl n pentadecafluorocarbonyl , octadecyl ester, 2,5 Dichloro N ethyl N phenyl benzamide, 2 Thiophenylacetic acid, 2,2,2 trifluoroethyl ester, Methyl 8 5 methoxycarbonyl methyl 2 furyl octanoate. It was concluded that the bioactive compounds support the use of C. hispidum leaves in the treatment of diseases like cancer, anaphylactic shock, renal failure, diabetes and hypertension. O. V. Ikpeazu | I. E. Otuokere | K. K. Igwe "GC–MS Analysis of Bioactive Compounds Present in Ethanol Extract of Combretum Hispidum (Laws) (Combretaceae) Leaves" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31868.pdf Paper Url :https://www.ijtsrd.com/chemistry/biochemistry/31868/gc–ms-analysis-of-bioactive-compounds-present-in-ethanol-extract-of-combretum-hispidum-laws-combretaceae-leaves/o-v-ikpeazu
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pre...CrimsonPublishers-PRM
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pregnancy by Darshit G Prajapati in Perceptions in Reproductive Medicine_Crimson Publishers
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pre...CrimsonPublishers-PRM
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pregnancy by Darshit G Prajapati in Perceptions in Reproductive Medicine_Crimson Publishers
UOG Journal Club: Reassessing critical maternal antibody threshold in RhD alloimmunization: a 16-year retrospective cohort study
C. A. Walsh, B. Doyle, J. Quigley, F. M. McAuliffe, J. Fitzgerald, R. Mahony, S. Higgins, S. Carroll and P. McParland
Volume 44, Issue 6, pages 669–673, December 2014
http://onlinelibrary.wiley.com/doi/10.1002/uog.13383/abstract
Agreement of two-dimensional and three-dimensional transvaginal ultrasound with magnetic resonance imaging in assessment of parametrial infiltration in cervical cancer
V. Chiappa, A. Di Legge, A.L. Valentini, B. Gui, M. Micco, M. Ludovisi, C. Giansiracusa, A.C. Testa and L. Valentin
Volume 45, Issue 4, pages 459–469, April 2015
http://onlinelibrary.wiley.com/doi/10.1002/uog.14637/abstract
Clinical implementation of routine screening for fetal trisomies in the UK NHS: cell-free DNA test contingent on results from first-trimester combined test
M. M. Gil, R. Revello, L. C. Poon, R. Akolekar and K. H. Nicolaides
Volume 47, Issue 1; pages 45–52
Link to free-access article: http://onlinelibrary.wiley.com/doi/10.1002/uog.15783/full
Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks' gestation: comparison with NICE guidelines and ACOG recommendations
N. O'Gorman, D. Wright, L. C. Poon, D. L. Rolnik, A. Syngelaki, M. de Alvarado, I. F. Carbone, V. Dutemeyer, M. Fiolna, A. Frick, N. Karagiotis, S. Mastrodima, C. de Paco Matallana, G. Papaioannou, A. Pazos, W. Plasencia, K. H. Nicolaides
Volume 49, Issue 6, Pages 756–760
Slides prepared by Dr Fiona Brownfoot (UOG Editor-for-Trainees)
Read the free-access article: http://onlinelibrary.wiley.com/doi/10.1002/uog.17455/full
Thanks to an excellent cost/benefit ratio, dedicated MRI systems have become a reality in theworld of diagnostic imaging.
The aging of the population together with growing sports activities at all ages has led to an ever-increasing demand for extremity MRI examinations.
Ultrasonographic Cervical Length Measurement at 10-14- and 20-24-weeks’ Gesta...AI Publications
Preterm labor is a regular occurrence in pregnancy; an estimated 15 million babies are born prematurely each year, with the number increasing. This was a prospective study of pregnant women who came to the Maternity Teaching Hospital in Erbil, Kurdistan Province, Iraq, for an outpatient clinic. On a manageable sample of 150 singleton pregnancies. In this study, one hundred fifty singleton asymptomatic pregnancies encountered the inclusion criteria during the study period, 69 primi gravid, 81 multi gravid. The correlation between the cervical length at 20–24 weeks and preterm delivery was moderately poor (r =0.715), and this correlation was highly significant (P < 0.001). In another word, a better correlation was found between preterm delivery and cervical length at 20–24 weeks than at 10–14 weeks in the prediction of preterm delivery. This study also points towards the importance of serial ultrasound scans to detect those who are at higher risk. There was no statistically significant effect of age, parity. Finally, the findings revealed that trans vaginal ultrasound is more accurate at 20-24weeks than 10-14weeks gestation for prediction of preterm labor, it can be used routinely to prevent preterm birth.
The Paperless partograph – The new user-friendly and simpler tool for monitor...iosrjce
IOSR Journal of Dental and Medical Sciences is one of the speciality Journal in Dental Science and Medical Science published by International Organization of Scientific Research (IOSR). The Journal publishes papers of the highest scientific merit and widest possible scope work in all areas related to medical and dental science. The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others.
GC–MS Analysis of Bioactive Compounds Present in Ethanol Extract of Combretum...ijtsrd
Phytoconstituents present in the ethanolic extract of Combretum hispidum leaves were explored by Gas Chromatography Mass Spectrometry analysis. The compounds were identified by the gas chromatography coupled with the mass spectrometry. The molecular weight and structure of the compounds of Combretum hispidum leaves were ascertained by interpretation of the spectrum of GC MS using the database of National Institute of Standard and Technology NIST . GC MS analysis of Combretum hispidum leaves revealed the presence of nineteen biological active compounds. The compounds are N Tosyl dl 3,4 dehydroprolylglycine, ethyl ester, 1 n Butoxy 2,2,3,3 tetramethylaziridine, 2 Butenoic acid, 3 methyl 4 tetrahydro 3,4 dihydroxy 5 3 2 hydroxy 1 methylpropyl oxiranyl methyl 2H pyran , Cobalt, octacarbonyl zinc di , 2Co Zn , 6 Dehydroxy 2,3,3,4,4,5,7 hepta O methylisoorientin, 2 naphthalenol, 3 5 3 nitrophenyl 1,3,4 oxadiazol 2 yl , L Proline, N 1 naphthoyl , dodecyl ester, 3,6 Dispirocyclohexyl 1,2,3,4,5,6,7,8 octahydro 1,8 acridinedione, Sarcosine, N 2 chloroethoxycarbonyl , heptadecyl ester, Cycloocta 1,2 b 4,3 b 5,6 b 8,7 b tetrakis 1 benzothiophene, Butanoic acid, 2 chloro 3 methyl , 4 5 heptyl 2 pyrimidinyl phenyl ester, Friedooleanan 1 one, 3,24 dihydroxy , 9 O Methyl 4,5 deoxymaytansino, Ditelluride, di 1 naphthalenyl, tert Butylstibinous acid thioanhydride, l Leucine, N methyl n pentadecafluorocarbonyl , octadecyl ester, 2,5 Dichloro N ethyl N phenyl benzamide, 2 Thiophenylacetic acid, 2,2,2 trifluoroethyl ester, Methyl 8 5 methoxycarbonyl methyl 2 furyl octanoate. It was concluded that the bioactive compounds support the use of C. hispidum leaves in the treatment of diseases like cancer, anaphylactic shock, renal failure, diabetes and hypertension. O. V. Ikpeazu | I. E. Otuokere | K. K. Igwe "GC–MS Analysis of Bioactive Compounds Present in Ethanol Extract of Combretum Hispidum (Laws) (Combretaceae) Leaves" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31868.pdf Paper Url :https://www.ijtsrd.com/chemistry/biochemistry/31868/gc–ms-analysis-of-bioactive-compounds-present-in-ethanol-extract-of-combretum-hispidum-laws-combretaceae-leaves/o-v-ikpeazu
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pre...CrimsonPublishers-PRM
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pregnancy by Darshit G Prajapati in Perceptions in Reproductive Medicine_Crimson Publishers
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pre...CrimsonPublishers-PRM
Comparison of Various Clinical Methods of Birth Weight Estimation in Term Pregnancy by Darshit G Prajapati in Perceptions in Reproductive Medicine_Crimson Publishers
UOG Journal Club: Reassessing critical maternal antibody threshold in RhD alloimmunization: a 16-year retrospective cohort study
C. A. Walsh, B. Doyle, J. Quigley, F. M. McAuliffe, J. Fitzgerald, R. Mahony, S. Higgins, S. Carroll and P. McParland
Volume 44, Issue 6, pages 669–673, December 2014
http://onlinelibrary.wiley.com/doi/10.1002/uog.13383/abstract
Agreement of two-dimensional and three-dimensional transvaginal ultrasound with magnetic resonance imaging in assessment of parametrial infiltration in cervical cancer
V. Chiappa, A. Di Legge, A.L. Valentini, B. Gui, M. Micco, M. Ludovisi, C. Giansiracusa, A.C. Testa and L. Valentin
Volume 45, Issue 4, pages 459–469, April 2015
http://onlinelibrary.wiley.com/doi/10.1002/uog.14637/abstract
Clinical implementation of routine screening for fetal trisomies in the UK NHS: cell-free DNA test contingent on results from first-trimester combined test
M. M. Gil, R. Revello, L. C. Poon, R. Akolekar and K. H. Nicolaides
Volume 47, Issue 1; pages 45–52
Link to free-access article: http://onlinelibrary.wiley.com/doi/10.1002/uog.15783/full
Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks' gestation: comparison with NICE guidelines and ACOG recommendations
N. O'Gorman, D. Wright, L. C. Poon, D. L. Rolnik, A. Syngelaki, M. de Alvarado, I. F. Carbone, V. Dutemeyer, M. Fiolna, A. Frick, N. Karagiotis, S. Mastrodima, C. de Paco Matallana, G. Papaioannou, A. Pazos, W. Plasencia, K. H. Nicolaides
Volume 49, Issue 6, Pages 756–760
Slides prepared by Dr Fiona Brownfoot (UOG Editor-for-Trainees)
Read the free-access article: http://onlinelibrary.wiley.com/doi/10.1002/uog.17455/full
Thanks to an excellent cost/benefit ratio, dedicated MRI systems have become a reality in theworld of diagnostic imaging.
The aging of the population together with growing sports activities at all ages has led to an ever-increasing demand for extremity MRI examinations.
Ultrasonographic Cervical Length Measurement at 10-14- and 20-24-weeks’ Gesta...AI Publications
Preterm labor is a regular occurrence in pregnancy; an estimated 15 million babies are born prematurely each year, with the number increasing. This was a prospective study of pregnant women who came to the Maternity Teaching Hospital in Erbil, Kurdistan Province, Iraq, for an outpatient clinic. On a manageable sample of 150 singleton pregnancies. In this study, one hundred fifty singleton asymptomatic pregnancies encountered the inclusion criteria during the study period, 69 primi gravid, 81 multi gravid. The correlation between the cervical length at 20–24 weeks and preterm delivery was moderately poor (r =0.715), and this correlation was highly significant (P < 0.001). In another word, a better correlation was found between preterm delivery and cervical length at 20–24 weeks than at 10–14 weeks in the prediction of preterm delivery. This study also points towards the importance of serial ultrasound scans to detect those who are at higher risk. There was no statistically significant effect of age, parity. Finally, the findings revealed that trans vaginal ultrasound is more accurate at 20-24weeks than 10-14weeks gestation for prediction of preterm labor, it can be used routinely to prevent preterm birth.
The Paperless partograph – The new user-friendly and simpler tool for monitor...iosrjce
IOSR Journal of Dental and Medical Sciences is one of the speciality Journal in Dental Science and Medical Science published by International Organization of Scientific Research (IOSR). The Journal publishes papers of the highest scientific merit and widest possible scope work in all areas related to medical and dental science. The Journal welcome review articles, leading medical and clinical research articles, technical notes, case reports and others.
Reliability, accuracy and cost effectiveness of prenatal screeningRustem Celami
Dr. Genc Kabili, Dr. Rustem Celami
A scientific paper in prenatal care
Prenatal screening, genetic abnormalities, reliability, accuracy, cost-effectiveness
Artificial Intelligence in OBGYN Keynote Address on 19th March 2022 at MOGS...Niranjan Chavan
Artificial Intelligence in OBGYN Keynote Address at the Mumbai ObGyn Society Golden Jubilee Annual Conference held at Hotel Trident, Nariman Point, Mumbai, India.
Breast Cancer Diagnostics with Bayesian NetworksBayesia USA
The Wisconsin Breast Cancer Database (WBCD) is a widely studied (and publicly available) data set from the field of breast cancer diagnostics. The creators of this database, Wolberg, Street, Heisey and Managasarian, made an important contribution with their research towards automating diagnostics with image processing and machine learning.
Beyond the medical field, many statisticians and computer scientists have proposed a wide range of classification models based on WBCD. Such new methods have continuously raised the benchmark in terms of diagnostic performance.
Our white paper now reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before. We demonstrate how the BayesiaLab software can extremely quickly — and simply — create a Bayesian network model that is on par performance-wise with virtually all existing models that have been developed from WBCD over the last 15 years.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.There are a total of thirteen hospitals included in this review. These facilities have implemented vitals capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team
monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists
of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.
There are a total of thirteen hospitals included in this review. These facilities have implemented vitals
capture and the MEWS scoring system.
IVF is stressful and expensive and there is a continued need to improve outcome using all information technology available to improve outcomes , meet expectations and review management.
Use of the NEDOCS overcrowding scale in a pediatric ED. Marion Sills
Weiss SJ, Ernst AA, Johnson A, Sills MR. Use of the NEDOCS overcrowding scale in a pediatric ED. Society for Academic Emergency Medicine’s Annual Meeting, San Francisco, May 2006.
AN ANALYSIS OF OUTCOMES IN TWIN PREGNANCIES WITH ACTIVE FETAL SURVEILLANCE AN...Apollo Hospitals
The incidence of multiple gestations is increasing with
increasing maternal age and use of assisted reproduction
techniques. Selective fetal reduction of multifetal pregnancies is now widely practiced to reduce the higher order multiples to twins based on evidence from nonrandomised studies which suggests that this will improve the perinatal outcome. The proportion of twin pregnancies with unique fetal and maternal problems is therefore increasing. Optimising maternal, fetal and perinatal outcomes in twin pregnancies continues to be a formidable challenge in the present day clinical practice.
Similar to Informativni model verjetnosti | An informative probability model (20)
In the last decade, there has been a rapid refinement of the diagnosis of neck thyroid lesions, especially in the field of thyroid disease. Ultrasound plays a fundamental role in the management of thyroid nodules and tumors and in the approach to recurring neck problems after thyroidectomy. Specifically, ultrasound examination is the main tool for the indication of fine needle aspiration biopsy (FNA) of thyroid lesions, suspicious cervical lymph nodes and parathyroid glands. Moreover, besides its diagnostic role, ultrasound is currently used as a guidance procedure for ablative treatment of benign and malignant cervical lesions.
Peripheral nerve ultrasonography in patients with transthyretin amyloidosis MIDEAS
Objective: To systematically study peripheral nerve morphology in patients with transthyretin (TTR)
amyloidosis and TTR gene mutation carriers using high-resolution ultrasonography (US).
Methods: In this prospective cross-sectional study we took a structured history, performed neurological
examination, and measured peripheral nerve cross-sectional areas (CSAs) bilaterally at 28 standard locations
using US. Demographic and US findings were compared to controls.
Results: Peripheral nerve CSAs were significantly larger in 33 patients with familial amyloid polyneuropathy
(FAP) compared to 50 controls, most dramatically at the common entrapment sites (median
nerve at the wrist, ulnar nerve at the elbow), and in the proximal nerve segments (median nerve in
the upper arm, sciatic nerve in the thigh). Findings in 21 asymptomatic TTR gene mutation carriers were
less marked compared to controls, with CSAs being larger only in the median nerve in the upper arm.
Nerve CSAs correlated with abnormalities on nerve conduction studies.
Conclusion: Using US, we confirmed previous pathohistological and imaging reports in FAP of the most
pronounced peripheral nerve thickening in the proximal limb segments.
Significance: Similar to US findings in diabetic and vasculitic neuropathies these predominantly proximal
locations of nerve thickening may be attributed to ischaemic nerve damage caused by poor perfusion in
the watershed zones along proximal limb segments.
https://www.linkedin.com/pulse/ultrasonographic-study-peripheral-nerves-bulgarian-mitja-dobovi%C4%8Dnik?trk=mp-author-card
Vascular Biomarkers in Cardiovascular Risk Prediction & Radiofrequency-based ...MIDEAS
Role of Vascular Biomarkers in
Cardiovascular Risk Assessment
The use of cardiovascular biomarkers in conjunction with risk scores
is expected to refine the risk stratification of an individual subject and
to guide his therapy. Biomarker is a characteristic that is objectively
measured and that reflects early functional and structural changes
in cardiovascular system, before overt disease manifestation. Vascular
biomarkers may be particularly informative, as they detect organ
damage in different parts of vascular bed, are measurable in a noninvasive
way, and reflect both aging process and adverse impact of
established cardiovascular risk factors, like plasma lipids, smoking,
high blood pressure, diabetes, inflammation1-2.
Nowadays, several vascular biomarkers have been proposed. According
to a position paper from the European Society of Cardiology
Working Group on peripheral circulation, the choice of vascular
biomarker or a combination depends on the clinical setting and
present comorbidities, and may differ for each individual patient3.
An insight in Eacvi-Ase-Industry Initiative to Standardize Deformation ImagingMIDEAS
Deformation imaging, based on Speckle Tracking techniques, is
a promising technology for the evaluation and quantification of
cardiac mechanics. In particular, during the last decade , a growing
body of scientific evidences has shown that this technology,
can provide incremental information in many clinical settings1,2,3.
However, still the main drawback for a fully clinical exploitation
of the technique is nowadays represented by the perception that
global strain measurements differ between vendors. Reasons for
this potential difference could be found in the different tracking
algorithms, differences in values definition as well as the underestimation
of the impact that some imaging acquisition parameters,
such as the images acquisition FR, Telediastolic triggering
frame positioning, may have on the final results.
Advanced diagnostic ultrasound system with eHD technology and CrystaLine configuration.
What is CrystaLine?
Improved imaging
in difficult to scan
patients.
Major technical improvements
provided by CrystaLine include the
CPI Technology to increase depth
of field, improving the imaging of
deep structures in difficult-to scan
individuals.
Cardiac Measurements Guidelines | powered by EsaoteMIDEAS
Complete routine cardiac measurements Guidelines.
1) Left Ventricle:
a) Size: Dimensions or volumes, at end-systole and end-diastole
b) Wall thickness and/or mass: Ventricular septum and left ventricular posterior wall thicknesses (at end-systole and end-diastole) and/or mass (at end-diastole)
c) Function: Assessment of systolic function and regional wall motion. Assessment
of diastolic function
2) Left Atrium:
• Size: Area or dimension
3) Aortic Root:
• Dimension
4) Right Ventricle:
Size: Dimensions
Function: Systolic and diastolic function
RV & pulmonary hemodynamics
5) Right Atrium:
a) Size: Dimensions, area
b) RA pressure
6) Valvular Stenosis:
a) Valvular Stenosis: Assessment of severity, including trans-valvular gradient and area.
b) Subvalvular Stenosis: Assessment of severity, Including subvalvular gradient.
7) Valvular Regurgitation: Assessment of severity with semi-quantitative descriptive statements and/or quantitative measurements
8) Cardiac Shunts: Assessment of severity. Measurements of QP:QS (pulmonary-to systemic flow ratio) and/or orifice area or diameter of the defect are often helpful.
9) Prosthetic Valves:
a) Transvalvular gradient and effective orifice area
b) Description of regurgitation, if present
Nova linija ultrazvočnih aparatov Esaote, razreda MyLab40.
Ultrazvočni aparat MyLab 40 Blue Edition je kompaktni konzolni sistem z zmogljivo platformo in visoko stopnjo mobilnosti. Njegova modularna zasnova omogoča optimalno prilagoditev potrebam uporabnika.
Visoko občutljivi barvni in pulzni doppler ter CW in PW doppler se prikažejo na 19" LCD monitorju, ki poveča uporabnikovo udobje in zmanjša naprezanje oči.
Velik nabor dodatnih aplikacij in perifernih enot na ultrazvočnem aparatu MyLab 40 BE pomeni vsestranskost uporabe in primernost za vse proračune, brez ogrožanja kvalitete prikaza ali enostavnosti uporabe.
Ultrazvočni aparat MyLab 40 Blue Edition je namenjen vrsti aplikacij:
Kardiologija
Splošna interna / Radiologija
Ginekologija in porodništvo
Intervencijska kirurgija
Revmatologija
Regionalna anestezija
Žilna diagnostika
Neonatologija / Pediatrija
Ortopedija
www.mideas.si
Of all the various techniques used for imaging just water and fat, the most common one is based on selective frequency excitation of water and fat tissues.
This technique is known as Fat Saturation.
Esaote’s continuous research for more specialized sequences together with high-speed acquisition, has consequently led to developing a technique for separating water and fat in a single scanning, combined with an appropriately optimized correction of the static magnetic field inhomogeneity.
This sequence has been called XBone.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Ocular injury ppt Upendra pal optometrist upums saifai etawah
Informativni model verjetnosti | An informative probability model
1. Med Bio Eng Comput (2008) 46:109–120
DOI 10.1007/s11517-007-0299-2
ORIGINAL ARTICLE
An informative probability model enhancing real time
echobiometry to improve fetal weight estimation accuracy
G. Cevenini Æ F. M. Severi Æ C. Bocchi Æ
F. Petraglia Æ P. Barbini
Received: 4 May 2007 / Accepted: 28 November 2007 / Published online: 10 January 2008
Ó International Federation for Medical and Biological Engineering 2007
Abstract A multinormal probability model is proposed to
correct human errors in fetal echobiometry and improve the
estimation of fetal weight (EFW). Model parameters were
designed to depend on major pregnancy data and were
estimated through feed-forward artificial neural networks
(ANNs). Data from 4075 women in labour were used for
training and testing ANNs. The model was implemented
numerically to provide EFW together with probabilities of
congruence among measured echobiometric parameters. It
enabled ultrasound measurement errors to be real-time
checked and corrected interactively. The software was useful for training medical staff and standardizing measurement
procedures. It provided multiple statistical data on fetal
morphometry and aid for clinical decisions. A clinical protocol for testing the system ability to detect measurement
errors was conducted with 61 women in the last week of
pregnancy. It led to decisive improvements in EFW accuracy.
Keywords Probability model Á Neural networks Á
Ultrasound Á Echobiometry Á Fetal weight estimation
1 Introduction
Many decisions in obstetrics depend on gestational age
(GA) and fetal weight (FW). Accurate ultrasound
G. Cevenini (&) Á P. Barbini
Department of Surgery and Bioengineering, University of Siena,
Viale Mario Bracci 16, 53100 Siena, Italy
e-mail: cevenini@unisi.it
F. M. Severi Á C. Bocchi Á F. Petraglia
Department of Pediatrics, Obstetrics and Reproductive
Medicine, University of Siena,
Viale Mario Bracci 16, 53100 Siena, Italy
examination performed before 20 weeks of gestation
enables true GA to be estimated [42]. On the other hand,
estimation of FW (EFW) using standard biometric
parameters, usually related to geometric dimensions of the
fetal head, abdomen and long bones of extremities, is still
problematical [18].
Monitoring of fetal growth is fundamental in modern
perinatology because it is strictly related to fetal/neonatal
wellbeing [43]. Moreover, identification of abnormal
intrauterine growth patterns enables better pregnancy
management [10, 21, 43].
In the last 30 years, many methods have been developed
to improve EFW accuracy, most based on formulae derived
by regression analysis [3, 16, 22, 23, 25, 27, 33, 35, 36, 38,
41, 44], or on physical models [2, 14, 17, 29]. Artificial
neural networks (ANNs) and volumetric methods based on
three-dimensional (3D) ultrasonography were also recently
proposed [11, 20, 40].
Clinical use of these mathematical models led to introduction of EFW in ultrasound reports. Although effective
in the original papers, ultrasound operators know that every
estimation model loses efficacy when applied in clinical
practice [9, 17]. The differences between accuracies in the
literature and those obtained in local clinical institutions
are due to many factors, the ones being significant statistical dissimilarity between original and local populations
and samples, diversities in echobiometric measurement
procedures and lack of model generalization. Little attention has usually been paid to generalization, which refers to
a model ability to provide the same accuracy on data not
used for model identification [5]. Specifically, empirical
formulae do not guarantee a good compromise between
model flexibility to fit all useful information and robustness
to filter useless data variability. Too many model
parameters have been estimated from few ultrasound cases
123
2. 110
near delivery. Sometimes fetuses with non-homogeneous
weight or GA intervals not representative of the whole
population are used. In other cases the clinical condition of
women in labour is neglected or incorrectly reported.
Although attempts to reduce statistical sample errors and
lack of generalization power by selecting the most accurate
and representative models have been made, a percentage
mean absolute error less than 7–8% of the true BW has
never been achieved in current clinical practice, with 25%
(or more) of estimates having an absolute error over 10%
[29]. Unfortunately, since most obstetricians take 10% as a
critical error threshold above which EFW cannot guarantee
correct clinical management, the method cannot yet be
considered reliable for clinical decision-making [7, 17].
Though many attempts have been made to reduce estimation errors by means of models specialized in particular
ranges of FW or GA [16, 23, 36], or derived from sophisticated 3D and ANN methods [11, 40], it has not proven
possible to significantly reduce the error, because it is presumably due to many different unpredictable factors (human,
environmental, instrumental, technological, etc.) associated
with digital processing of echobiometric values [17].
Since the 10% error limit for all populations of fetuses is
not so far away, there is great interest in finding solutions
that could improve EFW accuracy enough to reach the goal.
Actually, the only way to enhance fetal weight prediction accuracy seems to be reduction of operator
measurement error. Indeed, readings made by operators
with long experience in fetal ultrasound have significantly,
but not still sufficiently, lower errors.
This paper describes a computerized information system
to help ultrasound operators in the control and interactive
correction of measurement errors in two-dimensional fetal
biometry. It is based on a Gaussian multivariate (multinormal) probability model, the parameters of which are
identified by ANNs trained with sample data representing a
wide fetal population. Therefore, it properly belongs to
machine learning methods which are widely used in computing applications to support clinical decision making.
The effective level of real time improvement in the accuracy of EFW was tested clinically in a small sample of
pregnant women.
2 Methods
2.1 Population and samples
To design the model we used data of 4,075 fetuses in the
last week before birth, recorded in our clinics over the last
10 years. Only fetuses with evident malformations were
excluded from the database which was divided into three
samples equally representative of the fetal population:
123
Med Bio Eng Comput (2008) 46:109–120
a training set and a validation set of the same size from the
first 3,200 fetuses, the former by odd positions and the
latter by even positions of the chronologically ordered list;
the last 875 cases constituted a testing set. The training and
validation sets were used for model training, whereas the
testing set was used to check that model performance
remained statistically equivalent with new data (generalization ability). Finally, the system was applied in clinical
practice to 61 pregnant women in the last week before
delivery to verify its effective capacity to support interactive correction of real-time ultrasound measurements and
to improve EFW accuracy.
2.2 Measurement variables
Fetal echobiometric data, including biparietal diameter
(BPD), head and abdominal circumferences (HC, AC), and
femur length (FL), were measured by transabdominal
ultrasound scan with a Siemens Sonoline Elegra Millenium
Edition ultrasound system or a MYLAB Family instrument
(ESAOTE spa, Genova, Italy). Gestational age (GA) in
weeks was established by accurate menstrual history confirmed by ultrasound examination before the 20th week of
gestation. True FW was determined by measuring birth
weight (BW) with a precision balance soon after the
delivery. BW was the dependent variable used to train our
model to estimate FW from ultrasound scans just before
delivery.
Essential pregnancy data, namely amniotic fluid volume
(AF), number of fetuses (FN) and number of days between
last ultrasound examination and delivery (US-D) were also
entered in the training process.
AF was conceived as a binary-coded qualitative variable
with four categories: normal, absent, reduced and augmented volume. US-D ranged from 0 (i.e. ultrasound
examination and delivery on the same day) to 6 (i.e.
ultrasound examination 6 days before delivery).
2.3 Multinormal probability model
To describe the probability space of the ultrasound measurements we used the multivariate Gaussian density
function:
pðx=wÞ ¼
1
d=2
ð2pÞ jRðwÞj1=2
&
'
1
exp À ½x À lðwÞŠT RÀ1 ½x À lðwÞŠ
w
2
ð1Þ
where T is the vector transposition operator, d = 5 the
parameter space dimension, x = [BPD HC AC FL GA] the
3. Med Bio Eng Comput (2008) 46:109–120
111
vector of current echobiometric parameters, w = [BW AF
FN US-D] an information vector conditioning density
function (1), and l ðwÞ and R ðwÞ the mean vector and
covariance matrix, respectively, of parameters which
depend on w and have to be estimated to completely define
the probability model (1).
2.4 Artificial neural networks
Three feed-forward ANNs were designed to estimate the
parameters l ðwÞ and R ðwÞ of the multivariate normal
model. They were made sufficiently flexible (sufficient
number of hidden neurons and appropriate functions of
neuron activation) to encompass all deterministic data
patterns. Proceeding by trial and error, we selected ANN
architecture having ten neurons in a single hidden layer. It
offered a good compromise between simplicity and
generalization ability through error minimisation. Hidden
neurons were equipped with biased tansig activation
functions. The output neurons had linear activation for
estimating model parameters. The input data were standardized before presentation to the network, so as to have
zero mean and unit standard deviation. Standardization has
been shown to increase the efficiency of ANN training [6].
The first ANN, ANN1, was designed to estimate the
model mean vector, l ðwÞ; for each combination of
pregnancy information w, considered as input data. A
block diagram of ANN1 is shown in Fig. 1, where the
Fig. 1 Block diagram of the
feed-forward ANN training
process
training (T) and prediction (P) phases are in the upper and
lower left sides, respectively. Specifically, ANN1 is
trained to recognize the set of echobiometric measurements x, i.e. BPD, HC, AC, FL and GA, from input data
w, i.e. BW, AF, FN and US-D. Once trained, ANN1
predicts the corresponding most likely (expected) parameter values "; i.e. BPD; HC; AC; FL and GA; for any a
x
given set of pregnancy information. These expected values
are assumed as a reliable estimation of the mean parameter vector l ðwÞ: The ANN1 prediction phase is
reported in Fig.1 because it is necessary to obtain
parameter deviations, [xi - li(w)], (i = 1, 2,…, 5), namely
the differences between an echobiometric measurement,
xi, and its corresponding mean value, li, estimated by
ANN1 as a function of input data w. In the centre of Fig. 1
the calculation of deviations is illustrated, together with
their squared values, i.e. deviances di = [xi - li(w)]2, and
all their paired products, i.e. codeviances didj = [xi li(w)]Á[xj - lj(w)] (i = j = 1, 2,…, 5).
The two remaining ANNs, ANN2 (upper right side of
Fig. 1) and ANN3 (lower right side of Fig. 1), were then
trained to recognize deviances and codeviances, respectively. Once trained, ANN2 and ANN3 could therefore
estimate the expected values of deviances and codeviances, E{[xi - li(w)]2} and E{[xi - li(w)]Á[xj - lj(w)]},
respectively, which were taken as suitable estimations of
variances r2 and covariances rirj of model covariance
i
matrix R ðwÞ: Of all the pregnancy information, only BW
was assumed to affect the model covariance matrix. It is
BPD
2
δ BPD
HC
BW
(T)
2
δ HC
AF
ANN1
δ i2
AC
FN
2
δ AC
FL
ANN2
BW
2
δ FL
GA
(T)
2
δ GA
US-D
- - - - US-D
(P)
GA
FL
FN
ANN1
AC
AF
HC
BW
BPD
δi δj
δ BPDδ HC
δ BPD δAC
δ BPD δFL
δ BPD δGA
δ HC δAC
δ HC δ FL
δ HC δGA
δ AC δFL
δ AC δGA
δ FL δGA
(T)
ANN3
BW
123
4. 112
well-known that the inferential process exploits a reduction
of data dimensions, especially when a large number of
parameters (matrix elements) have to be estimated [6].
Significantly improved accuracy of estimates largely
compensates for the lack of other pregnancy information.
ANN2 and ANN3 were therefore equipped with a single
BW input (see right of Fig. 1). Their prediction phase is not
reported in Fig. 1, to avoid unnecessary detail.
All the ANNs were trained using a batch training
method which updates synaptic weights and neuron biases
only after all inputs and targets have been presented, i.e.
after each iteration. An iterative training algorithm with
gradient descendent momentum and adaptive learning rate
was used to minimise the mean squared error between real
and predicted outputs.
To limit the influence of training algorithm initialization
on the solution, we performed 99 training sessions starting
from 99 different randomly-selected initial values of ANN
parameters (i.e. synaptic biases and weights), and chose the
session giving the median error value (50th sorted value).
The early-stopping method was applied directly during
the training process to control ANN generalization power
and avoid the problem of overfitting [6, 24]. At each iteration, training and validation errors were calculated from
data used to train the ANN (training set) and to validate
generalization (validation set), respectively. Training was
stopped when the validation error did not decrease for ten
consecutive iterations. Testing data was then used to confirm generalization on a third set of cases that had not been
used during training.
2.5 Fetal weight estimation
The principal aim of this study was to predict FW, which
was strictly related to BW for training ANNs. BW is the
first component of pregnancy information vector w and
cannot be known for an unborn fetus.
In the case of a fetus, whose mathematical expressions
will be denoted with an upper symbol *, knowledge of the
~
other three components of vector w; that is AF, FN and USD, and its measured echobiometric parameters, ~; allows
x
ANN1 to identify the vector of expected parameters,
~
lðBWÞ; as a function of unknown BW. It identifies five
monotonic curves on which five expected values of BW
can be found corresponding to actual measurements ~; they
x
are expressed by the five-dimensional vector BWexp.
The most probable value of BW, BWmp, corresponding
to ~; can be derived from model (1) by calculating the
x
volume of the confidence region in parameter space, as
follows. Once the available pregnancy data of information
~
vector w are known, volume depends only on its first
unknown component, BW, and describes the cumulative
123
Med Bio Eng Comput (2008) 46:109–120
~
conditional probability of x representing the strength of
association between true fetal weight and its just-measured
ultrasound parameters. The higher the volume, the more
measurements are expected to be mutually congruent and
accurately related to the associated weight.
The confidence region can be described mathematically
by considering the scalar quantity in the exponential term
of model equation (1):
Q ¼ dT RÀ1 d
ð2Þ
where d = x - l represents the vector of generic parameter deviations.
Q is a quadratic form which was demonstrated to be
2
distributed as dðn À1Þ times a Fisher density function, F,
nðnÀdÞ
with d and (n - d) degrees of freedom [28]. In our application, the number of fetuses n, used for model designing,
was much greater than the parameter space dimension d, so
that the valid approximations (n2 - 1) % n2 and (n 2
d) % n, and therefore dðn À1Þ ffi d; were used for simplifynðnÀdÞ
ing. Thus, the confidence region at probability level a can
be defined as the locus of parameter deviations, d; which
satisfy the following inequality:
Q
À1
d Fc ðd; n; aÞ
ð3Þ
F-1
c
where
is the inverse of cumulative F distribution, Fc,
with d and n degrees of freedom and evaluated at the
probability level a.
Equation (3) describes a five-dimensional hyperellipsoidal region.
The probability, ~; defines the volume of the hyperela
lipsoid on whose surface the current measurements, ~; lie.
x
It can be derived by inverting Eq. (3):
~
~ ¼ Fc ðd; n; Q=dÞ
a
ð4Þ
~
~
where Fc has evaluated at the value Q=d and Q is calcu~ ¼ ~ À l:
lated from formula (2) using d x
The quadratic form of (3) implies a unique maximum,
~max ; for ~: It corresponds to a value of BW necessarily
a
a
located in the interval between the minimum and the
maximum value of vector BWexp. Though ~max could thea
oretically be evaluated analytically, for practical reasons
we did a numerical search among all ~ values correa
sponding to the same number, N, of BW sampling values,
spaced at steps, DBW, of 10 g, that is
N
~max ¼ maxi f~ðBWi Þg
a
a
1
È
É
BW1 ¼ min BWexp
È
É
BWN ¼ max BWexp
BWiþ1 ¼ BWi þ DBW; DBW ¼ 10 g
ð5Þ
BWmp was chosen to correspond with the region of
maximum probability volume, ~max ; and was assumed as
a
5. Med Bio Eng Comput (2008) 46:109–120
113
the current EFW, even long before birth. It represents the
most plausible value of FW associated with the available
pregnancy information and the current echobiometric
measurements, taken together.
~
The vector, l ¼ lðBWmp Þ; of expected parameter values
evaluated at BWmp, provides model deviations, ~m ¼ ~ À
d
x
~
l; from actual measurements, and their probabilities, ~m ;
a
which account for measurement errors and morphological
characteristics of fetal physiopathology.
~m can be derived by projecting the multivariate normal
a
model (1) along any generic parameter axes, xk (k = 1,
2,…, 5), as follows:
(
)
~
1
1 ð x k À lk Þ 2
pðxk =wÞ ¼ pffiffiffiffiffiffiffiffiffiffi exp À
ð6Þ
2
~k
r2
2p~2
rk
~
~
~k
where lk is of course the kth component of l and r2 is the
corresponding variance from the principal diagonal of
~
covariance matrix R ¼ RðBWmp Þ:
Any component ~m;k of vector ~m can therefore be cala
a
culated from (6):
8
Z xk
~
>
>1 À 2
>
~ ~
pðxk =wÞ
if xk lk
<
À1
~m;k ¼
ð7Þ
a
Z þ1
>
>1 À 2
>
~
~
pðxk =wÞ
if xk [ lk
:
~
xk
Accuracy of EFW was evaluated by computing the mean
absolute percentage error, MAE%:
MAE% ¼
N
X AEi
 100
i
N
1
experience) was chosen to perform fetal biometry. Ultrasound data were entered in the model to evaluate the
probability of agreement among measured fetal biometric
parameters and actual EFW.
On the basis of clinical evidence, model-estimated
maximum probability, ~max ; corresponding to the most
a
probable EFW (i.e. BWmp) and congruence probabilities of
the parameters, ~m ; the operators decided autonomously
a
whether or not to correct the first set of measurements and
to proceed with further refined measurements. Specifically,
for each set, ~; of measured echobiometric parameters, the
x
operator was suggested to consider possible measurement
errors when at least one of the ~m parameter probabilities
a
was less than 50% or when the EFW probability, ~max ; was
a
less than 50%. In this case, the operator decided to make
new ultrasound measurements or to keep the current
measurements, depending on his/her clinical experience
and on case-specific clinical information.
Improvements of accuracy in EFW were assessed by
applying our interactive method on-line to the 61 abovementioned pregnant women in the last week before delivery. We calculated mean and maximum AE% (MAE% and
AEmax%) and the percentage of FW having AE% greater
than 10% (AEgt10%).
The effectiveness of measurement error correction was
also evaluated using some mathematical models from the
literature [3, 14, 22, 25, 33, 35, 44] proven to give performance equivalent to our model by error comparison
using the non parametric statistical test of Wilcoxon [1].
ð8Þ
jEFWi À BWi j
AEi ¼
BWi
3 Results
where AEi is the relative absolute error of the model in
predicting the i-th fetal weight.
3.1 Model estimation of fetal weight
2.6 Clinical evaluation of model performance
Our method for real-time control of fetal echobiometry was
then tested for its effective ability to detect and correct
measurement errors and therefore improve accuracy in
EFW.
Ultrasound parameters of 61 fetuses were evaluated
within 5 days of delivery in the Department of Pediatrics,
Obstetrics and Reproductive Medicine, University of Siena, by real-time interaction with our multinormal model,
implemented numerically by software developed in Matlab
language [19].
To investigate whether the system was able to appropriately correct measurement errors difficult to detect and
to significantly improve the accuracy of EFW, an obstetrician with good experience in ultrasound (at least 2 years
Model performance was statistically equivalent for the
training, validation and testing data sets (Wilcoxon test,
p [ 0.05). We therefore report the results for the entire
data set used for model design. Figure 2 shows the distribution of percentage error in relation to birth weight for the
multinormal probability model and the seven models which
gave statistically equivalent performance on the 61 data
items used for evaluating our model in real-time clinical
practice. Table 1 gives the MAE% and the percentage of
cases with AE% greater than 10% (AEgt10%) for each
model. As we can see (Fig. 2), only our proposed multinormal model, by virtue of its probability nature, has
uniform non-biased behaviour over the whole range of
BW. On the contrary, all the other models based on
regression techniques have an error distribution strongly
influenced by training data density in BW space, with the
only exception being the Hadlock model, which has
moderate bias because it was trained on a data set having a
123
6. 114
Med Bio Eng Comput (2008) 46:109–120
Fig. 2 Distribution of
percentage error in relation to
birth weight in our multinormal
model and the other seven
models selected to give
statistically equivalent
performance with our clinical
data
quite uniform BW distribution [22]. Table 1 shows that this
model had errors very similar (MAE% = 7.81, AEgt10% =
30.8%) to our model (MAE% = 7.86, AEgt10% = 31.3%).
In particular, Fig. 2 shows that the Ott [33], Combs [14],
Woo [44] and Robson [35] models overestimate low BWs
and underestimate high BWs, whereas the Hill [25] and
Benson [3] models have different biases, underestimating
low and high BWs and overestimating intermediate BWs.
123
The lowest performances in Table 1 are shown by models
particularly biased at high BWs. Cases with high errors
generally also had low probabilities associated with our
model EFW, presumably due to ultrasound measurement
errors. Probability region boundaries with low probability
values are therefore an inspection area in which measurement errors should be checked and where the accuracy of
EFW could improve.
7. Med Bio Eng Comput (2008) 46:109–120
115
Table 1 Model performance evaluated on the whole set of data
(training, validation and testing sets) used to design the multinormal
model
MAE%
AEgt10%
Multinormal
7.86
31.3
Ott
7.45
27.2
Combs
Hill
8.43
8.00
33.1
29.7
Woo
7.53
28.7
Benson
8.43
32.6
Hadlock
7.81
30.8
Robson
7.74
30.0
Model
Mean absolute percentage error MAE%; percentage of fetuses estimated to have an AE% greater than 10% AEgt10%
A prototypical numerical implementation of our model
is shown in Fig. 3 that reports the screen hard copy of
graphical user interface of the underlying software. In the
right side of Fig. 3 we have gestational information ð~ Þ;
w
actual measurements ð~Þ; probabilities of congruence
x
among them ð~m Þ and their model-estimated expected
a
values ð~Þ: The lower the probability of parameter conl
gruence, the more suspect that parameter has to be
considered. High deviation ð~m Þ from expected values may
d
be due to measurement errors. Excessively low probability
values or low values of more than one parameter suggest
that the ultrasound session should be repeated. Figure 3
(left side) shows the five plot windows of most probable
parameter values (black lines) and standard deviations
(light blue lines) in relation to BW, as estimated from
ANNs. Dots around curves represent training data. On the
top of the graphic windows are the EFW (BWmp) and its
multivariate probability ð~max Þ: Again, the lower this
a
probability, the more high measurement errors, or unusual
body conformation, or both, can be expected. When ~max is
a
particularly low, at least one of the congruency probabilities ~m is low as well. Dashed blue lines underline both
a
EFW (BWmp, vertical lines) and its corresponding modelestimated expected parameter values ð~; horizontal lines).
l
At the bottom of each plotting area, the univariate expected
EFWs (BWexp, vertical dashed red lines) are reported with
the measured parameter values ð~; horizontal dashed red
x
lines). The multivariate most probable EFW, BWmp, is of
course between the minimum and maximum of five univariate BWexp values.
Figure 3 shows an example of EFW by our system. It
concerned a fetus at 40 weeks. The system indicates that
measured head circumference (HC = 350 mm) has a low
probability (10%) of being congruent with respect to other
fetal biometric parameters and an EFW of 3,331 g (probability 13%). This could mean: (1) that the HC
measurement is incorrect and that it needs to be measured
again; (2) that fetal HC is correct but is bigger than
expected because of hereditary predisposition; (3) that HC
is bigger for pathological reasons. Only the operator
experience, if necessary with other clinical information,
can answer this question.
Fig. 3 Graphic user-interface
of interactive software for fetal
echobiometry control and
correction, to improve EFW
accuracy
123
8. 116
Med Bio Eng Comput (2008) 46:109–120
3.2 Clinical evaluation of model performance
experienced operator. After correction (excepting two
models), the percentage of cases with an error above 10%
reduced to zero, as shown in Table 2. Maximum error was
lower or just a little higher than 10%.
In 16 out of 61 cases (26.3%) fetal biometry was measured
once and in 45 cases it was repeated two or more times, to a
total of 153 measurements. System performance was
assessed by comparing its 61 initial FW estimates with
those obtained without (16 cases) or with one (3 cases) or
more (42 cases) re-measurements of ultrasound parameters
associated with low (less than 50%) congruence probabilities. For comparison we used EFW, derived from 182
formulas (from 59 published papers) [17]. Considering the
61 initial estimates, seven formulas [3, 14, 22, 25, 33, 35,
44] showed a performance statistically equivalent to our
system (Wilcoxon test, P [ 0.05). All other formulas gave
significant higher errors. Table 2 shows the performances
of all models. It is evident that correction of detected errors
yielded statistically significant improvements not only in
our model EFW (MAE% from 6.5% to 2.6%) but also when
the new biometry was tested by the seven best models (i.e.
Hadlock formula MAE% from 6.7% to 3.5%), thus confirming that the system is able to correct measurement
errors that affect model performance, worsening their
accuracy.
In particular, although the Hadlock model showed the
second best decrease in MAE% after our system, we found
a drastic reduction in error variability, with a maximum
error of 9.0% (in the same fetus), lower than that made by
our system (maximum error of 10.7%). Nevertheless, this
maximum error of 10.7% is acceptable, because it concerns
a normal weight fetus (real weight 3,640 g) that was
underestimated by the system (EFW equal to 3,250 g).
Other models also showed very good performance with
few errors above 10%.
In the cases we analyzed, MAE% was low at initial
estimations because the measurements were made by an
4 Discussion
Accurate prediction of BW by ultrasonographic measurement of classical fetal morphometric parameters plus other
related pregnancy data, such as gestational age, amniotic
fluid volume and number of fetuses, is of considerable
interest in obstetrics, enabling clinicians to more accurately
predict infant morbidity and mortality [17]. Moreover,
EFW in utero is of great clinical interest for monitoring
fetal growth [31, 34] and may have a central role in major
medical decisions in critical conditions of preterm delivery
and fetal macrosomy [15, 20, 35, 36].
Although a lot of sophisticated mathematical formulas
and models have been developed in the last 30 years [3, 11,
14–17, 20, 22, 23, 25, 27, 29, 33, 35, 36, 38, 41, 44],
estimates still typically have too high an error variance,
preventing reliable clinical use [13, 15, 17, 29]. Even
operators with proven ability in ultrasound examination
provide remarkably high percentages (15–25%) of fetuses
whose BW is estimated with an AE% greater than 10%.
This problem seems difficult to overcome because the
many errors of fetal ultrasound evaluation are presumably
due to technological, environmental, intra- and interobserver variability in fetal measurement and so forth [17,
29]. There are currently unlikely to be major revolutions in
technology, ultrasonographic practice and other methods
that could significantly improve accuracy of measurements
and/or their ability to predict BW more reliably. At the
moment, it is not at all easy to quantify errors, and
Table 2 Model performance evaluated in 61 pregnancies before (initial measurements) and after (ultimate measurements) zero (16 cases), one
(3 cases), or more corrections (42 cases) of the initial ultrasound measurements
Model
Initial measurements
Ultimate measurements
MAE%
AEmax%
AEgt10%
MAE%
AEmax%
AEgt10%
Multinormal
6.5
19.3
13.1
2.6
10.7
1.6
Ott
5.7
16.9
9.8
4.3
9.6
0.0
Combs
Hill
5.8
6.2
19.1
18.6
11.5
13.1
4.2
4.6
12.2
10.2
1.6
1.6
Woo
6.6
20.1
18.0
4.6
9.8
0.0
Benson
6.7
19.1
16.4
4.9
13.6
3.3
Hadlock
6.7
18.1
16.4
3.5
9.0
0.0
Robson
6.7
16.8
16.4
5.4
14.7
9.8
Corrections were decided autonomously by the operator using an interactive system based on the proposed multinormal model for fetal weight
estimation: absolute percentage AE%; mean absolute percentage error MAE%; maximum absolute percentage error AEmax%; percentage of
fetuses estimated to have AE% greater than 10% AEgt10%
123
9. Med Bio Eng Comput (2008) 46:109–120
particularly to discriminate errors due to intra- and interobserver variability in ultrasound measurements. Efforts
must be made to minimise this variability if EFW is to be
considered clinically useful [17].
Many recent attempts have been made to reduce the
estimation error on lower and higher FWs, where the
clinical interest is of course focused. In general, clinicians
distinguish these two critical intervals of weight from an
intermediate one that typically ranges from 2,500 to
4,000 g [16, 20, 23]. Almost all models for EFW exhibit a
worsening of accuracy in critical weight classes (below
2,500 g and above 4,000 g) where lower/higher weights
are usually over/under-estimated [13, 16, 29]. Most mathematical models are derived from statistical regressions
and account nonlinearly for ultrasound measurements by
fitting experimental data. They are therefore most accurate
for intermediate weights, where experimental data has
higher density, and produce increasing biases going from
median to lower or higher FWs where data density progressively decreases. Concerning this problem it is really
important to underline that it is in the critical weight
classes that weight estimation becomes fundamental from a
clinical point of view. A dangerous increase of the rate of
false normal weights arises. In other words, such biased
models tend to reassure excessively about a normal FW,
correctly identifying only very critical conditions that can
be detected by simple qualitative investigations.
Models specialized in critical weight ranges have also
been constructed and tested: they are sometimes much
more accurate in the range where they have been fitted and
dramatically less accurate elsewhere, as would be expected
[15, 17, 23, 29, 35, 36, 38, 41]. The use of these specialized
models therefore requires prior knowledge about the
weight range in which to classify the fetus, leading to
dangerous amplification of errors in borderline areas which
are of critical clinical interest. This has also legal implications for ultrasonographers who may make gross errors
with severe consequences for maternal and fetal health.
Moreover, there have been several studies to evaluate
the efficacy of mathematical models related to specific GA
intervals [32, 41]. Although GA intervals are better defined
than weight intervals, they are nevertheless affected by
gestational age estimation precision, that becomes less
accurate as pregnancy goes on, and it is only partially
related to microsomic and macrosomic fetuses.
In our opinion, the use of mathematical models specialized for specific FW and/or GA ranges can therefore be
dangerous, of little clinical interest and not significantly
better than those applicable to the entire fetal population. In
other words, they are of no help.
All other efforts to decrease AE% by introducing correction factors in the algorithms and new information, such
as amniotic fluid volume, number of fetuses and maternal
117
pathologies, or non-routine echobiometric parameters, have
failed to bring effective improvements [8]. Moreover, more
recent mathematical models, besides the above mentioned
limits, are sometimes based on echobiometric parameters
difficult to obtain, particularly by unskilled operators [8,
37, 40]. Specifically, three-dimensional (3D) ultrasound
enables volumetric parameters such as fetal thigh, upper
arm and abdomen to be measured for EFW. Although
preliminary studies seems to indicate improvements [40],
doubts remain about the utility of 3D for a substantial
improvement in the accuracy of EFW [17]. Moreover, 3D
ultrasound systems are expensive, not as widespread as 2D
systems, and unfamiliar for operators doing routine fetal
biometry. In any case, if the superiority of 3D ultrasound
systems were established, our model could be easily
extended to volumetric measurements.
Today, about ten models are considered to give the best,
not significantly different performances and none give a
MAE% below 7–8% [15, 17, 29].
We chose to tackle the problem of reducing human error
in the use of ultrasound devices for fetal biometry by significantly improving the accuracy of EFW. An interesting
attempt to control ultrasound measurement errors by
enhancing the fetal border and reducing noise was recently
proposed for evaluation of nuchal translucency thickness
[30]. Its impact on fetal echobiometry for improving the
accuracy of EFW should be investigated.
We designed a weight-dependent Gaussian probability
model [1, 28] over the whole range of BWs, which avoids
the above-mentioned biases and provides detailed information about the reliability of measurements through
interactive software, allowing redefinition of measurements
and real-time correction. Model parameters were estimated
from a large database of 3,000 fetuses, collected by ultrasound operators of proven experience, though presumably
containing measurement errors. Our hypothesis was that by
correcting or limiting these errors, we could obtain an EFW
of acceptable accuracy to protect fetal and maternal health
and reduce wrong medical decisions, which sometimes also
have legal implications.
In line with Dudley [17], we consider that insufficient
accuracy in EFW depends on excessive intra- and interobserver variability of measurements. The great advantage
of using a multivariate Gaussian model is that it assigns
probability values to the different ultrasound measurements
and to EFW. The model is designed and trained on ultrasound data measured by experienced ultrasound operators
who carefully followed the standardised protocols for
correct echobiometry [4]. It can therefore guide operators
to follow its reliable statistical representation suggesting
repetition of divergent readings to reduce errors. We
assumed that human errors occur more frequently in the
space of ultrasound measurements where the model
123
10. 118
indicates lower probabilities of congruence among biometric parameters. However, low probabilities can also
arise from fetal pathology or peculiar morphology, such as
maternal diabetes, unusual parental build and abnormal
fetal growth. Though these zones may not be distinguished
by ultrasound examination alone, they are both of great
clinical interest. Thus, when operators encounter low
model probabilities, they are alerted to investigate more
thoroughly than usual and to repeat suggested biometric
measurements. Two distinct situations are possible so that
new measurements can be: (1) the same as before and/or
still associated with low probabilities; (2) substantially
different but in the direction of model expected values,
increasing the probability of congruence with other fetal
parameters. In the first case, there may be abnormalities
suggesting the need to review other clinical data, such as
maternal/paternal build and pathologies. In the second
case, measurement errors may be detected and corrected. In
both situations, at least a third session of measurements is
recommended for confirmation. If any disagreement still
remains between measurement sessions, operators should
decide on the basis of other clinical information and/or
experience.
Since our method incorporated certain clinical information about pregnancy, it was convenient to use an ANN
approach [24] to estimate multinormal model parameters
(i.e. mean vectors and covariance matrices), that were
made to depend on pregnancy data and FW. The model
dependence on pregnancy information gives a more accurate probability but makes the problem of estimating its
parameters from sample data unfeasible with common
statistical methods, such as multivariate regression, which
would be inaccurate. For example, means of the parameter
vector could be estimated by entering pregnancy variables
in multivariate linear regression models where echobiometric measurements are assumed as dependent variables.
Unfortunately, all regression techniques are very sensitive
to empty regions in observation space and to outliers [1, 5,
6, 12, 28], and are most accurate where observations are
densest. Since in clinical application there is great interest
in regions with low data density, e.g. macrosomic and
microsomic fetuses, we choose an ANN approach to
overcome the many limits of regression technique [6, 24,
26]. ANNs are sophisticated machine learning methods
which make it possible to express the knowledge contained
in experimental data with great flexibility and precision,
and provide a uniform description, without discontinuities,
of the input-output relationship. They can therefore determine expected output values with satisfactory accuracy, by
interpolating missing data even in multivariate space with
few sparse observations [6]. Other important advantages of
ANNs with respect to statistical regression models are that
it is not necessary to specify model structure, hypotheses
123
Med Bio Eng Comput (2008) 46:109–120
about statistical data distribution are unnecessary, they are
able to describe nonlinearities, naturally take correlation of
input variables into account and can be trained with
examples like humans [24, 26, 39]. ANNs have recently
been successfully applied in many fields of medicine. All
that is required is a sufficiently large, representative set of
training examples. The main difficulty with ANNs is their
training which must be done with care to avoid overfitting,
a tendency of ANNs to learn even training data variability
which cannot be generalized to the whole phenomenon.
There are many methods of ensuring ANN generalization
power, for example regularization techniques, growing and
pruning algorithms, genetic algorithms and early-stopping
(ES) procedures [6, 26]. We applied the ES which is widely
used to train ANNs by virtue of its fast computational time
[6, 24]. It divides the available data into training and validation sets. Generalization is ensured by stopping the
training process at the iteration when the ANN begins to
overfit, that is when the error computed on the validation
set starts to increase. However, since the validation set is
involved in the training process in any case, it must not be
used for estimating the generalization error. We therefore
tested the ANNs with the third set of data (testing set)
which had not been used during training [6].
When we tested our model in clinical practice to correct
operator measurement errors in real time, we obtained very
encouraging results. Fetal biometric measurements were
performed by an experienced operator because we wanted
to understand whether under optimum conditions, it was
possible to obtain errors below 10%. We were successful in
this endeavour.
The fact that we obtained a significant lowering of
MAE% when we fitted the corrected parameters in the best
estimation models of the literature, confirms that our system can in fact help operators to correct measurement
errors. The system also promises to be useful for training
less experienced sonographers and could be used as a
quality control system for fetal biometry. By reducing
human error, it enhances EFW and clinical obstetric
management.
5 Conclusions
A multinormal probability model for the estimation of fetal
weight was implemented numerically to provide clinical
indications about the type and size of measurement errors
in real-time fetal echobiometry. The model compared
actual measures with expected values and associated
probability values with EFW, indicating the reliability of
EFW in terms of congruence with ultrasound measurements. Low probabilities suggest more accurate repetition
of suspect measurements and help ultrasound operators to
11. Med Bio Eng Comput (2008) 46:109–120
interpret fetal morphology by distinguishing between
measurement errors and real pathophysiological
conditions.
Compared to other EFW models of equivalent accuracy,
probability models also have the major clinical advantage
of avoiding over- and under-estimation of micro- and
macrosomic fetal weights.
Clinical testing of the model on a sample of 61 fetuses
revealed its good performance in correcting measurement
errors and showed a remarkable improvement in accuracy of
EFW, confirmed by other mathematical models of proven
accuracy. Our proposed interactive software therefore offers
valid support for training operators in fetal echobiometry.
Although system capacity clearly needs to be tested on a
wider scale, its clinical utility and simplicity, as well as the
sharp improvement in accuracy of EFW, suggest that it
could be used as a reliable auxiliary for clinical decision
making in pregnancy. This is also an advance in the direction of standardization of measuring procedures, which are
often a severe limiting factor in ultrasonographic practice.
119
11.
12.
13.
14.
15.
16.
17.
18.
Acknowledgments This work was financed by the Italian Ministry of
Education, University and Research (MIUR). Special thanks to ESAOTE S.p.A., Genoa, Italy, for its precious and prompt technical support.
19.
References
21.
1. Armitage P, Berry G (1987) Statistical methods in medical
research. Blackwell, Oxford
2. Ben-Haroush A, Yogev Y, Hod M (2004) Fetal weight estimation
in diabetic pregnancies and suspected fetal macrosomia. J Perinat
Med 32(2):113–121
3. Benson CB, Doubilet PM, Saltzman DH (1987) Sonographic
determination of fetal weights in diabetic pregnancies. Am J
Obstet Gynecol 156(2):441–444
4. Bettelheim D, Deutinger J, Bernaschek (1997) Fetal sonographic
biometry: a guide to normal and abnormal measurements. The
Parthenon Publishing Group
5. Biagioli B, Scolletta S, Cevenini G, Barbini E, Giomarelli P,
Barbini P (2006) A multivariate Bayesian model for assessing
morbidity after coronary artery surgery. Crit Care 10(3):R94. doi:
10.1186/cc4951
6. Bishop HCM (1995) Neural networks for pattern recognition.
Clarendon, Oxford
7. Chauhan SP, Hendrix NW, Magann EF, Morrison JC, Kenney
SP, Devoe LD (1998) Limitations of clinical and sonographic
estimates of birth weight: experience with 1034 parturients.
Obstet Gynecol 91(1):72–77
8. Chauhan SP, West DJ, Scardo JA, Boyd JM, Joiner J, Hendrix
NW (2000) Antepartum detection of macrosomic fetus: clinical
versus sonographic, including soft-tissue measurements. Obstet
Gynecol 95(5):639–642
9. Chauhan SP, Hendrix NW, Magann EF, Morrison JC, Scardo JA,
Berghella V (2005) A review of sonographic estimate of fetal
weight: vagaries of accuracy. J Matern Fetal Neonatal Med
18(4):211–220
10. Chauhan SP, Cole J, Sanderson M, Magann EF, Scardo JA (2006)
Suspicion of intrauterine growth restriction: use of abdominal
22.
20.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
circumference alone or estimated fetal weight below 10%. J Matern Fetal Neonatal Med 19(9):557–562
Chuang L, Hwang JY, Chang CH, Yu CH, Chang FM (2002)
Ultrasound estimation of fetal weight with the use of computerized artificial neural network model. Ultrasound Med Biol
28(8):991–996
Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple
regression: correlation analysis for the behavioral sciences. Erlbaum, London
Colman A, Maharaj D, Hutton J, Tuohy J (2006) Reliability of
ultrasound estimation of fetal weight in term singleton pregnancies. New Zeal Med J 119(1241):U2146
Combs CA, Jaekle RK, Rosenn B, Pope M, Miodovnik M, Siddiqi TA (1993) Sonographic estimation of fetal weight based on a
model of fetal volume. Obstet Gynecol 82(3):365–370
Coomarasamy A, Connock M, Thornton J, Khan KS (2005)
Accuracy of ultrasound biometry in the prediction of macrosomia: a systematic quantitative review. Brit J Obstet Gynaec
112(11):1461–1466
Dudley NJ (1995) Selection of appropriate ultrasound methods
for the estimation of fetal weight. Brit J Radiol 68:385–388
Dudley NJ (2005) A systematic review of the ultrasound estimation of fetal weight. Ultrasound Obstet Gynecol 25(1):80–89
Edwards A, Goff J, Baker L (2001) Accuracy and modifying
factors of the sonographic estimation of fetal weight in a highrisk population. Aust NZ J Obstet Gyn 41(2):187–190
Etter DM, Kuncicky DC, Moore H (2005) Introduction to
MATLAB 7. Prentice Hall, Englewood Cliffs
Farmer RM, Medearis AL, Hirata GI, Platt LD (1992) The use of
a neural network for the ultrasonographic estimation of fetal
weight in the macrosomic fetus. Am J Obstet Gynecol
166(5):1467–1472
Goldberg JD (2004) Routine screening for fetal anomalies:
expectations. Obstet Gynecol Clin North Am 31(1):35–50
Hadlock FP, Harrist RB, Sharman RS, Deter RL, Park SK (1985)
Estimation of fetal weight with the use of head, body, and femur
measurements - a prospective study. Am J Obstet Gynecol
151:333–7
Hadlock FP (1990) Sonographic estimation of fetal age and
weight. Fetal Ultrasound 28(1):39–51
Haykin S (1994) Neural networks: a comprehensive foundation.
Maxwell Macmillian, Canada
Hill LM, Breckle R, Gehrking WC, O’Brien PC (1985) Use of
femur length in estimation of fetal weight. Am J Obstet Gynecol
152:847–852
Jamshidi M (2003) Tools for intelligent control: fuzzy controllers, neural networks and genetic algorithms. Philos Transact A
Math Phys Eng Sci 361(1809):1781–1808
Jordaan HV (1983) Estimation of fetal weight by ultrasound.
J Clin Ultrasound 11(2):59–66
Krzanowski WJ (1988) Principles of multivariate analysis: a
user’s perspective. Clarendon, Oxford
Kurmanavicius J, Burkhardt T, Wisser J, Huch R (2004) Ultrasonographic fetal weight estimation: accuracy of formulas and
accuracy of examiners by birth weight from 500 to 5000 g.
J Perinat Med 32(2):155–161
Lee YB, Kim MJ, Kim MH (2007) Robust border enhancement
and detection for measurement of fetal nuchal translucency in
ultrasound images. Med Biol Eng Comput (Spec issue). doi:
10.1007/s11517-007-0225-7
Lockwood CJ, Weiner S (1986) Assessment of fetal growth. Clin
Perinatol 13(1):3–35
Mongelli M, Biswas A (2002) Menstrual age-dependent
systematic error in sonographic fetal weight estimation: a mathematical model. J Clin Ultrasound 30(3):139–44
123
12. 120
33. Ott WJ, Doyle S, Flamm S, Wittman J (1986) Accurate ultrasonic
estimation of fetal weight. Prospective analysis of a new ultrasonic formula. Am J Perinatol 3(4):307–10
34. Ott WJ (2006) Sonographic diagnosis of fetal growth restriction.
Clin Obstet Gynecol 49(2):295–307
35. Robson SC, Gallivan S, Walkinshaw SA, Vaughan J, Rodeck CH
(1993) Ultrasonic estimation of fetal weight: use of targeted
formulas in small for gestational age fetuses. Obstet Gynecol
82(3):359–364
36. Rosati P, Exacoustos C, Caruso A, and Mancuso S (1992)
Ultrasound diagnosis of fetal macrosomia. Ultrasound Obstet
Gynecol 2(1):23–29
37. Rotmensch S, Celentano C, Liberati M, Malinger G, Sadan O,
Bellati U, Glezerman M (1999) Screening efficacy of the subcutaneous tissue width/femur length ratio for fetal macrosomia in
the non-diabetic pregnancy. Ultrasound Obstet Gynecol
13(5):340–344
38. Sabbagha RE, Minogue J, Tamura RK, Hungerford SA (1989)
Estimation of birth weight by use of ultrasonographic formulas
targeted to LGA, AGA, and SGA fetuses. Am J Obstet Gynecol
160:854–862
39. Sargent DJ (2001) Comparison of artificial neural networks with
other statistical approaches: results from medical data sets.
Cancer 91(S8):1636–1642
123
Med Bio Eng Comput (2008) 46:109–120
40. Schild RL, Fimmers R, Hansmann M (2000) Fetal weight estimation by three-dimensional ultrasound. Ultrasound Obstet
Gynecol 16(5):445–452
41. Secher NJ, Djursing H, Hansen PK, Lenstrup C, Sindberg-Eriksen P, Thomsen BL, Keiding N (1987) Estimation of fetal weight
in the third trimester by ultrasound. Eur J Obstet Gynecol Reprod
Biol 24:1–11
42. Sladkevicius P, Saltvedt S, Almstrom H, Kublickas M, Grunewald C, Valentin L (2005) Ultrasound dating at 12–14 weeks of
gestation. A prospective cross-validation of established dating
formulae in in vitro fertilized pregnancies. Ultrasound Obstet
Gynecol 26(5):504–511
43. Thornton JG, Hornbuckle J, Vail A, Spiegelhalter DJ, Levene M,
GRIT study group (2004) Infant wellbeing at 2 years of age in the
growth restriction intervention trial (GRIT): multicentred randomised controlled trial. Lancet 364(9433):513–520
44. Woo JS, Wan MC (1986) An evaluation of fetal weight prediction using a simple equation containing the fetal femur length.
J Ultrasound Med 5(8):453–457