Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur.
ARTIFICIAL INTELLIGENCE(AI) IN RADIOLOGY.pptxHillaryFrancis
Radiology, the branch of medicine that uses medical imaging techniques to diagnose and treat diseases. Radiology is experiencing a transformative revolution with the integration of Artificial Intelligence (AI).
AI, the ability of computer systems to perform tasks that normally require human intelligence, is revolutionizing the field of radiology by enhancing diagnostic capabilities, optimizing workflow efficiency, and improving patient outcomes.
Radiology plays a vital role in healthcare by utilizing medical imaging techniques to diagnose and monitor diseases. However, with the advent of artificial intelligence (AI), the field of radiology is experiencing a significant revolution. In this presentation, we will delve into the transformative impact of AI in radiology and how it is revolutionizing healthcare as we know it.
PRESENTED BY
HILLARY FRANCIS
DDR SDH
Role of artificial intellegence (a.i) in radiology department nitish virmaniNitish Virmani
Machine Learning and Deep Learning is the key to Artificial Intelligence. Future of Radiology with Artificial Intelligence and advancements of Radiology Equipments
Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur.
ARTIFICIAL INTELLIGENCE(AI) IN RADIOLOGY.pptxHillaryFrancis
Radiology, the branch of medicine that uses medical imaging techniques to diagnose and treat diseases. Radiology is experiencing a transformative revolution with the integration of Artificial Intelligence (AI).
AI, the ability of computer systems to perform tasks that normally require human intelligence, is revolutionizing the field of radiology by enhancing diagnostic capabilities, optimizing workflow efficiency, and improving patient outcomes.
Radiology plays a vital role in healthcare by utilizing medical imaging techniques to diagnose and monitor diseases. However, with the advent of artificial intelligence (AI), the field of radiology is experiencing a significant revolution. In this presentation, we will delve into the transformative impact of AI in radiology and how it is revolutionizing healthcare as we know it.
PRESENTED BY
HILLARY FRANCIS
DDR SDH
Role of artificial intellegence (a.i) in radiology department nitish virmaniNitish Virmani
Machine Learning and Deep Learning is the key to Artificial Intelligence. Future of Radiology with Artificial Intelligence and advancements of Radiology Equipments
Presentation that gives an overview of the impact of IT on radiology, including the growing role of biomarkers and artificial intelligence and deep learning on the (future) radiology profession. The shift to precision medicine and personalized care are explained, the reasons for a re-definition of radiology are addressed.
HQ Imaging helps radiology and research centers to obtain high-precision medical images, which are needed for diagnosis, therapeutic decision-making and fundamental research progress. Based on their in-depth expertise in magnetic resonance imaging (MRI), HQ Imaging offers two main services: quality assurance and protocol optimization. Whether you need a shorter scan time per patient or you would like to improve image quality, the HQ Imaging team can tune every MRI sequence to your needs. In addition, the assured image quality means less repeat scans (e.g artefact reduction) and faster radiological readings (e.g. better contrast to noise ratio, adaption of geometrical parameters).
A picture archiving and communication system (PACS) is a medical imaging technology which provides economical storage and convenient access to images from multiple modalities.
In this presentation we are going to talk about:-
1-What is PACS?
2-History of PACS
3-Before and after PACS
4-PACS architecture
5-PACS integration with RIS
6-PACS and DICOM
7-Advantages and disadvantages
Presentation that gives an overview of the impact of IT on radiology, including the growing role of biomarkers and artificial intelligence and deep learning on the (future) radiology profession. The shift to precision medicine and personalized care are explained, the reasons for a re-definition of radiology are addressed.
HQ Imaging helps radiology and research centers to obtain high-precision medical images, which are needed for diagnosis, therapeutic decision-making and fundamental research progress. Based on their in-depth expertise in magnetic resonance imaging (MRI), HQ Imaging offers two main services: quality assurance and protocol optimization. Whether you need a shorter scan time per patient or you would like to improve image quality, the HQ Imaging team can tune every MRI sequence to your needs. In addition, the assured image quality means less repeat scans (e.g artefact reduction) and faster radiological readings (e.g. better contrast to noise ratio, adaption of geometrical parameters).
A picture archiving and communication system (PACS) is a medical imaging technology which provides economical storage and convenient access to images from multiple modalities.
In this presentation we are going to talk about:-
1-What is PACS?
2-History of PACS
3-Before and after PACS
4-PACS architecture
5-PACS integration with RIS
6-PACS and DICOM
7-Advantages and disadvantages
Changing Medical profession with Artifical Intelligence what it means to us Dr.T.V.Rao MD
•Artificial Intelligence fast penetrating to every system and modality of human living However the implications of Artificial Intelligence is truly different from other professions we should be more aware of the ongoing matters and chose what is good in Human and health care ?
•Dr.T.V.Rao MD
•Former professor of Microbiology
•Adviser and Member Associate Elsevier research Netherlands
Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare.
Gleecus Whitepaper : Applications of Artificial Intelligence in HealthcareSuprit Patra
In the field of medicine, Artificial Intelligence (AI) goes a long way in strengthening and improvising the communication between Doctors and Patient like never before. The Healthcare industry requires enormous amounts of digitized data to be periodically shared, stored and yet kept secure at the same time. Smart algorithms are powering artificial intelligence (AI) applications in the healthcare sector By enabling intelligent applications to not only speak and listen but also to make decisions in unrivaled ways to nullify human errors.
Read this research paper to know how AI is taking healthcare by storm.
In this presentation I share the ideas regarding Radiology and AI relation to each other . Its helps to explore more about radiology . Its key advaantage is that u find a detailed knowledge of Radio Imaging and AI at the same platform . If u want to know about the healthcare and research centers where AI is used you also find the collabration among various AI TECHNOLOGY MANUFACTURERS and RESEARCH CENTERS . If you are doing graduation in AI or RADIOLOGY field , it well define your stream . It enhance the workfield arena for radigraphers and how they will increase their job profiles and clearly differntiate them between Robots and AI because most of our assests thought AI in healthcare leads to robotic work but they are totally wrong in this , they need to understand that AI is just going to decrease the work pressure on them . This will not going to take their jobs and our radio imaging machines are only operated by our medical proffesssionalists i.e. our Radiotechnician . It will reduce the waiting time for patients and it increase the job satisfaction for us. As a Radio Imaging student , I try to clear all the doubts regarding the job misconceptiopns in our mind. We must be prgressive , truthful and honest while our job responsibility. AI will help in faster Image Analysis , it helps in Diagnostic accuracy, it also helps in early detection of disease , as it will pre analysize the data from various modalities and make a 3D view of the image form . It leads to increase the patients trust on our healthcaere providers . It helps in unnecessary excessive radiation to the patients , leads to decrease in the risk of radiation related diseases , like skin erethema , skin cancer and skin infections . Yeah.. you also find some challenges regarding smart radiology but by doing proper cincern in your imaging field you will going to solve all the errors in it . We must try to organize short confrence in which we share our views and plans for future innovations and this presentation is small effort towards it. It will also control the quality of your images . This presentation will help you differntiate between the TELEMEDICINE and PACS . I also share the requireds tools and techniques which are using in radiological field . Some of them are - machine learning, deep learning, natural language processing, computer aided detection, image segmentation, 3D- Imaging analysis, automated repoting , radiomics, generative adversial networks . It will show you how AI effects life of Radiographers and Technicians , by stating : efficiency and productivity, workload management , skill enhancement, career evolution, job satisfaction, and patients care, quality assurance. Here , I also share some case studies where AI ia implemented in Radiology, it includes Cleveland Clinic collabration with Zebra Medical Vision , Stanford Medicine use of ARTERYS and UNIVERSITY OF CALIFORNIA , SAN FRANCISCO [ UCSF] and TEMPU . Whereas some challenges in adopting AI in Radiology.
AI Revolutionizing Healthcare Patient Care and Diagnostics.pdfJPLoft Solutions
Recent years have seen the incorporation of technology known as Artificial Intelligence (AI) into healthcare, bringing about the dawn of a new era that has revolutionized how patients receive care and diagnostics. This unique intersection between cutting-edge technology and medical science has an opportunity to enhance the efficacy, quality, and accessibility of health treatments.
Machine Learning and the Value of Health TechnologiesCovance
Machine learning can be applied through the development of algorithms that can unravel or "learn" complex associations in large datasets with limited human input. These algorithms are capable of making predictions that go beyond our capabilities as humans and they can process and analyze more possibilities. Machine learning may help us find answers to questions that we didn't even think of in the past, revealing evidence previously hidden among the data. We can use these methods to dig up imperceptible patterns and allow health technologies to be used at the right time and for the right patient population. (A4 Version)
Proposed Model for Chest Disease Prediction using Data Analyticsvivatechijri
Chest diseases if not properly diagnosed in early stages can be fatal. Because of lack of skilled
knowledge or experiences of real life practitioners, many a times one chest disease is wrongly diagnosed for the
other, which leads to wrong treatment. Due to this the actual disease keeps on growing and become fatal. For
example, muscular chest pains can be treated for the heart disease or COPD is treated for Asthma. Early
prediction of chest disease is crucial but is not an easy task. Consequently, the computer based prediction system
for chest disease may play a significant role as a pre-stage detection to take proper actions with a view to recover
from it. However the choice of the proper Data Mining classification method can effectively predict the early
stage of the disease for being cured from it. In this paper, the three mostly used classification techniques such as
support vector machine (SVM), k-nearest neighbour (KNN) and artificial neural network (ANN) have been studied
with a view to evaluating them for chest disease prediction.
Similar to Artificial intelligence-in-radiology (20)
Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision making.
Normal thyroid on US-
Homogenous with medium level echogenicity.
Thin hyperechoic capsule, which becomes calcified in pts with uremia or calcium metabolism disorder.
Superior and inferior thyroid artery and vein.
Mean diameter of artery 1-2 mm with PSV of 20-30 cm/s
Veins can ne dilated upto 10 mm.
The recurrent laryngeal nerve runs with inf thyroid artery and passes between esophagus and thyroid lobeon left side & logus coli and thyroid lobe on righjt side.
Scrotal Masses
98-100% accuracy in distinguishing intra and extra-testicular masses.
*** Most extratesticular masses are benign & most intratesticular masses are malignant
Malignant lesions are msotly hypoechoic.
Malignant neoplasia pts usually presents as
painless , unlateral testicular mass .
Clinically it is important to differentiate between Seminomas and Non Seminomatous germ cell tumors.
Grey scale Imaging – High frequency Transducers are used for most of peripheral veins (9 MHz). for iliac or inf venacava , transducer of 4-6 MHz are used. Superficial veins such as saphenous vein, calf veins need even higher frequency transducers ( 9-15 MHz).
Doppler Sonography – quantitative (duplex spectral) & qualitative (color Dopler) .
This combination of anatomic and physiologic information makes US-CD such a powerful tool in evaluation of vascular pathology.
The upper and lower extremity arteries , easy to examine, becoz of good imaging window.
Doppler frequencies are typically more than 3 MHz.
Though real-time gray-scale sonography is useful for evaluating the presence of atherosclerotic plaque or confirming the presence of extravascular masses. Color flow Doppler sonographic imaging allows the clinician to survey the area of interest rapidly, determine if vascular structures are present, and if so, characterize their blood flow patterns
Nuchal translucency
It is a sonographic pre natal screening scan to detect cardiovascular abnormality in a fetus.
NT can also detect altered extra cellular matrix composition and limited lymphatic drainage
G Sac seen within the thickened decidua .
Eccentric location within endometrium
Should abut the endometrial canal ( to differentiate it from decidual cyst )
On TVS -4& half -5 weeks
Thresold level – identifies the earliest one can expect to see a sac -4w3d
Discriminatory level – identifies when one should always see the sac- 5w 2d .
Ovulation was initially monitored by conventional methods like BBT, mid luteal serum progesterone and urinary LH.
Nowadays, USG is used for follicular monitoring for both natural and stimulated cycles.
By using transvaginal sonography, the bladder can be seen as early as 11 weeks of gestation. By 12 to 13 weeks, the bladder is visualized in 98% of cases using both transabdominal and transvaginal sonography.
Sonographic evaluation of fetal face is a part of anatomic survey in mid pregnancy
However , little is required; b/c according to american institute of ultrasound in modern practice guidelines, only visualization of fetal upper lip is mandatory during anatomy survey.
3D & 4D images are more informatory in cases where fetal face is hard to evaluate in 2D scan due to fetal position.
Malformations of Cortical Development
Cortex under goes complex development at neuronal/cellular level.
Neurons on outer surface of cortex undergoes 3 overlapping phases from 5th to 28th week.
Proliferation
Migration
organisation
Error of Dorsal Induction
Results in defect of closure of neural tube which leads to various anomalies like anencephaly, encephalocoele, spinal dysraphism and chiari malformations.
In many fetal skeletal dysplasias ,the skin and s/c tissue continues to grow at a rate proportionately greater than the long bones resulting in relatively thickened skin folds (on occasion mistaken for hydrops fetalis ) .
Polyhydraminos –common .cause –variable combination of the following –oesophageal compression by the small chest ,GI abnormalities ,micrognathia ,or hypotonia .
Generally occurs secondary to pulmonary atresia with intact IVS .
Pathophysiology- it develops because of a reduction in the blood flow secondary to inflow impedence from tricuspid atresia or outflow impedence from pulmonary arterial atresia .
Typical findings- a small , hypertrophic RV and a small or absent pulmonary artery
To study the morphological characteristics and enhancement patterns of probably malignant breast lesions on dynamic contrast enhanced MRI and to correlate the findings with Color Doppler imaging and histopathologically.
To evaluate importance of DWI in improving specificity of MR Breast.
4 BASIC TYPES OF DENSITY - air , water /soft tissues, metal /bone , fat
Two substances of the same density, in direct contact, cannot be differentiated from each other on an x-ray.
This phenomenon, the loss of the normal radiographic silhouette (contour), due to loss of difference in density is called the silhouette sign.
2 types (a) cellular NSIP
(b) Fibrotic NSIP (more common)
Fibrosis may involve alveolar septa, peribronchivascular interstitium, interlobular septa and visceral pleura.
Prognosis of fibrotic NSIP is worse , cellular NSIP has good prognosis.
HRCT finding may show both, airspace and interstitial patterns
Despite recent declines in its popularity, excretory urography still remains the cornerstone of radiological diagnosis of urinary tract
The strength of urography lies in its ability to provide overall survey of urinary tract; anatomic definition of the kidney, collecting system, and the lower urinary tract; as well as information about renal function
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.
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
- 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
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
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
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
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
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
3. Contents
Introduction01
What is artificial intelligence?02
How can AI help the radiologist03
How can AI help the radiologist help
patients
04
Current challenges for AI in radiology05
Bibliography06
4. Introduction
•Worldwide interest in artificial
intelligence (AI) applications, including
imaging, is high and growing rapidly,
fueled by availability of large datasets
(“big data”), substantial advances in
computing power, and new deep-
learning algorithms
•AI surveillance programs may help
radiologists prioritize work lists by
identifying suspicious or positive cases
for early review.
•Predictions have been made that
suggest AI will put radiologists out of
business. This issue has been
overstated, and it is much more likely
that radiologists will beneficially
incorporate AI methods into their
practices.
•Success for AI in imaging will be
measured by value created: increased
diagnostic certainty, faster turnaround,
better outcomes for patients, and better
quality of work life for radiologists.
•AI offers a new and promising set of
methods for analyzing image data.
Radiologists will explore these new
pathways and are likely to play a
leading role in medical applications of
AI.
5. What is artificial intelligence?
What is artificial intelligence and
how does it work?Depending on the context, several definitions for
artificial intelligence can be used. Many of these
definitions link human behavior to the (intended)
behavior of a computer. In the case of radiology
these definitions do not quite cover the scope of
AI as there are many situations where AI
exceeds human capabilities.
VISION
In radiogenomics, for example, we link genetic
information to what we see on medical images,
enabling us to predict the presence or absence
of genetic mutations in a tumor which can be
used to determine further diagnosis and
management
MISSION
“a branch of computer science
concerning the simulation of
intelligent human behavior in
computers”.
.
Definition 1:
a branch of computer science dealing with the acquisition,
reconstruction, analysis and/or interpretation of medical
images by simulating human intelligent behavior in
computers”
Definition 2:
Artificial intelligence is a field of
science, with machine learning being
an important sub-field, and deep
learning is a sub-field of machine
learning.
.
Definition 3:
6. Modern Portfolio
Presentation
Whenever AI is discussed, words
like machine learning, deep
learning and big data get thrown
around... but who knows his
machine learning from his deep
learning? Let’s get a clearer
picture of some of these
buzzwords. How do AI, machine
learning and deep learning relate
to one another? A schematic
overview of the field as a whole is
shown in Figure 1 .
7. Radiologists are extremely busy healthcare
professionals. They cannot afford to make any
mistakes. They need to interact with a wide
range of referring physicians; neurologists,
urologists, orthopedic practitioners, the list goes
on. They need to be sharp, always. What can
AI bring these stretched radiologists and make
them even better at what they do?
How can AI help
the radiologist?
8. What are the benefits of AI in radiology?
AI is not good at everything. At least not yet.
What are currently the best tasks to hand over
to AI? Tasks for which we have loads of data
available, that are fairly straightforward and do
not require combining a lot of different input.
Hence the simple routine tasks radiologists do
a lot. Usually this concerns the more
monotonous tasks, in other words the tasks
that radiologists find cumbersome.
Pick up repetitive routine tasks
Many AI solutions are focused on providing extra
information. This can be by quantifying information
enclosed in an image, where it is currently only
reported in a qualitative way. Or the software can add
normative values, allowing physicians to compare
patient results to an average based on a cross-section
of the population. The difficulty with this benefit is that
we do not always know yet how to handle this extra
information
Provide a more differentiated diagnosis
Even the best trained, most experienced radiologists might
differ in their diagnosis sometimes. Well rested in the
morning, something different might catch the attention than
after a long working day. Additionally, different radiologists
might emphasize different aspects in their reports. This can
be tricky for referring physicians, as they need to take into
account these variations when synthesizing all the
information they have, before coming to a final diagnosis.
AI software has the ability to decrease or even eliminate
this variability between radiologist reports.
Eliminate inter- and intra-observer variability
Having an AI algorithm run in the background offers
an easy way for obtaining a second opinion. The
algorithm results can serve as a simple backup check
on the diagnosis of the physician. An additional benefit
of having AI software running as a second opinion is
that it allows the radiologist to gradually get used to
working with AI and build trust as they see that it adds
value.
Offer a second opinion
There are several ways AI can advance the performance of radiologists even further. In this section we will discuss a few of these approaches.
This list is not exhaustive. There are many more ways for AI to benefit the radiologist such as:
9. How will AI realize
these benefits?
There are many tasks AI can perform in the context of
radiology. Some tasks will require just a medical image
as input and will base the analysis purely on the pixels
(or voxels). Others will go one step further and will
combine radiological images with information obtained
from other sources
Using only the image as input
AI that uses only a medical image as input, will deliver results that are
mostly similar to what radiologists otherwise would do manually. For
example, automatic segmentations of specific organs can be done
manually (e.g. liver and HCC segmentation to determine whether a
resection can and should be performed). However, these type of
analyses are very time consuming and therefore very suited to
“outsource” to an algorithm. Another example is the quantification of
specific distances (e.g. automatic measurement of RECIST scores).
Again this can be done manually, but many radiologists experience this
as monotonous task, making it a suitable candidate to get some AI
help.
Adding other information from other patient exams
Combining medical images with other information can lead to insights
that are not always easily to obtain for radiologists. These types of
analyses are usually considered more futuristic. For example, by linking
image data to pathology lab results it is possible to let an algorithm
derive pathology information from a medical image. Another example is
an algorithm that extracts genetic data from images without having
access to genetic markups of a patient.
A different type of analysis is adding normative information. For
example, by comparing patient organ volumes to the average of the
population. This can be useful in dementia research (comparing
volumes of specific brain structures to a normative database) or in case
of splenic enlargement.
10. How can AI help the radiologist help patients?
Each diagnostic process aims to realize the best patient outcomes. Medical imaging is increasingly part of the
diagnostic chain and should therefore be aimed at the exact same end goal: benefiting the patient. Hence for each
AI solution used by radiologists to assess images, we should do the litmus test and ask “At the end of the day,
does this software benefit the patient?” Simply put, you can think about patient benefits along two axes: the quality
and the efficiency axis. We will discuss both below.
Quality increase for better patient outcomes
AI offers great potential to increase quality of current
image readings. For example, by performing analysis
that are currently not performed because those are too
time consuming for radiologists to execute manually. An
example is volumetric measurements of organs, where
manual delineation is too demanding time wise, but
could improve the accuracy of the diagnosis.
Additionally, AI is an important enabler of precision
medicine. As more patient data becomes available, we
can determine in a more detailed way what information
implies certain treatments leading to better patient
outcomes. Another step in the process that can
improve with some AI influence is patient
communication. This is not necessarily directly the field
of the radiologist, however, radiologists can deliver
easier to understand reports to the referring physician
that can facilitate patient conversations.
Efficiency improvement to benefit the patient
Quality of care is extremely important, however, if the
diagnosis takes too long, great quality is of no use.
Therefore, quality should always be combined with efficiency.
AI can help increase efficiency in several ways. It can help
speed up the diagnosis process by automating tasks that are
time consuming when performed manually. For example,
RECIST score measurement can be a good candidate for
acceleration by automating the process. Another possibility is
to help the radiologist prioritize urgent cases. Which imaging
exams should the radiologist assess first? AI can do a first
assessment and move cases up the list if necessary.
02
AI
11. Will AI take over radiologist jobs?
It will pick up routine tasks which are
experienced as cumbersome by a lot of
radiologists. Yet, radiologists have a much
more differentiated job than these type of
tasks alone. Radiologist jobs will change,
but they will not disappear
.
it will not take over radiologist jobs.
However, it most certainly will take
over some radiologist tasks. It will
support radiologists by performing
automatic measurements which are
currently very time consuming
Simple answer, No,
12. What are current challenges for AI in radiology?
A smooth user-experience
A business case that adds up
The right performance
metrics
A sufficient amount of
quality labelled data
Dealing with a 3D
reality
Non-standardized
image acquisition
CHALLENGES
The sky seems to be the limit when it
comes to applying AI in radiology. But
for now there are still quite some
challenges to overcome before AI will be
widely applied and fully adopted in the
radiology workflow of which are:
The user's trust
13. What are current challenges for AI in radiology?
A sufficient amount of quality labelled data
Within the medical field, access to large high-quality labelled datasets for training is not straight
forward. Other general databases are extremely powerful because they include a vast amount of
images which are accurately labelled. Comparing the typical medical imaging dataset of approximately
1000 images to a non-medical database which can contain up to 100 000 000 pictures, it can only be
concluded that the volume available is clearly still several orders of magnitude behind. A way to
overcome this problem is using augmentation.
Figure : Big data can consist of many different types of data.
14. What are current challenges for AI in radiology?
Dealing with a 3D reality
The most successful deep learning models are currently trained on simple 2D pictures. CT- and
MRI-images are usually 3D, adding an extra dimension to the problem. Conventional X-ray
images may be 2D, however, due to their projected character, most of the current deep learning
algorithms are not adjusted to these images either. Experience needs to be gained with applying
deep learning to these types of images.
Non-standardized image acquisition
Varying scanner types, different acquisition settings… Non-standardized acquisition of medical
images creates a challenging situation for the training of artificial intelligence algorithms. The
more variety there is in the data, the larger the dataset needs to be to ensure the deep learning
network results in a robust algorithm. A method to tackle this barrier is to apply Transfer Learning,
which is a pre-processing technique aimed to overcome scanner and acquisition specifics.
15. What are current challenges for AI in radiology?
A smooth user-experience
One of the most frequent comments radiologists mention
are regarding their non satisfactory experience with
current radiology software. Why? Because it is generally
very user-unfriendly. It requires too much waiting time, too
many clicks, and once you are in the program you cannot
live without the manual by your side.
Creating a user friendly software is a must for AI
companies that want their software to be used in the clinic.
However, it is not as straightforward as it sounds. Many
applications are developed tech-first, meaning a company
starts with an algorithm and then turns it into a product.
This is not necessarily a bad approach, you’re certain
you’ve got a working algorithm, but testing the user-
friendliness of your product should be part of the product
development process, preferably from the start, to ensure
radiologists will start and keep using it in the clinic.
Figure : AI applications should ensure a smooth user
experience to encourage adoption
16. What are current challenges for AI in radiology?
A business case that adds up
How will the financial picture play out? Will hospitals have to allocate budget? Will the bill
eventually be presented to the patient? Or, the scenario we are aiming at, will AI pay for itself? It
is unlikely that AI radiology software will, in its current state, be able to fully support itself, mostly
due to the upfront investment that needs to be made. In this situation it is only obvious that the
investor wants to know what to expect on the long run, hence AI companies need to have a clear
view on how their software will financially benefit hospitals in the future: will it save physicians
time? Will the hospital be able to shorten waiting times and because of that help more patients?
Will diagnosis accuracy improve and cause savings at a later stage in the process? Are there
reimbursement codes available for the specific analysis
The right performance metrics
Every scientist will confirm you need to be clear on what you measure, how you measure it and,
probably most important, why you measure it. AI in radiology is no different from any other field of
research. AI companies need to be very clear on their performance measurements. Often used
metrics are accuracy, precision, recall, etc. However, these metrics do not always apply. Accuracy
is calculated using the amount of true positives, true negatives, false positives and false
negatives. In case of automated segmentation, there is no straight forward interpretation of a true
positive or a false negative. Hence metric selection is of utmost importance if you want to paint
17. What are current challenges for AI in radiology?
The right performance metrics
Every scientist will confirm you need to be clear on what you measure, how you measure it and,
probably most important, why you measure it. AI in radiology is no different from any other field of
research. AI companies need to be very clear on their performance measurements. Often used
metrics are accuracy, precision, recall, etc. However, these metrics do not always apply. Accuracy
is calculated using the amount of true positives, true negatives, false positives and false
negatives. In case of automated segmentation, there is no straight forward interpretation of a true
positive or a false negative. Hence metric selection is of utmost importance if you want to paint
the right picture on how your software performs.
The user's trust
However, possibly the most important challenge AI radiology companies are facing is the lack of
trust in artificial intelligence when it comes to answering questions related to medical image
analysis. AI is often seen as a “black box” of which it is unclear how it exactly came to its answer.
How can we ease the adoption of AI and strengthen the user's trust? There are several ways of
doing this: with scientific research, installing the software in the hospital and test run the
application. If AI companies want to survive in the field of radiology, they should invest in gaining
the user’s trust.
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