June 2018 version
How deep learning reshapes medicine
- Brief deep learning
- Recent applications
- Specific researches
- Perspectives and future directions
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
Sleep quality prediction from wearable data using deep learningLuis Fernandez Luque
Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S. Sleep Quality Prediction From Wearable Data Using Deep Learning. JMIR Mhealth Uhealth 2016;4(4):e125. http://doi.org/10.2196/mhealth.6562
The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
Sleep quality prediction from wearable data using deep learningLuis Fernandez Luque
Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S. Sleep Quality Prediction From Wearable Data Using Deep Learning. JMIR Mhealth Uhealth 2016;4(4):e125. http://doi.org/10.2196/mhealth.6562
The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
MedicalResearch.com: Medical Research Exclusive Interviews December 14 2014Marie Benz MD FAAD
MedicalResearch.com features exclusive interviews with medical researchers from major and specialty medical research and health care journals and meetings.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Study on Physicians Request for Computed Tomography Examinations for Patients...IRJESJOURNAL
Background and objectives: There is a lot controversy about the use of Computed tomography (CT) for patients with minor head injury. We aimed to determine the practice of guiding rules for the safety of radiation and increasing awareness of physicians about risks of ionizing radiation and find out the reasons of emergency doctors for sending head injury patients to CT scan exams. Materials and Methods: A descriptive questionnaire in the Emergency Department (ED) based study was performed to assess physicians' knowledge of radiation doses received from radiological treatments and knowledge about Clinic Decision Support rules (CDS). The questionnaire consisted of 26 questions distributed to physicians working in the emergency department in six hospitals in East Java. Finally, the data collected have been analyzed by some tests using SPSS version 15 and Smart PLS. Results: In this study 44 participants had taken part. The percentage of general knowledge and awareness that shows the response of people who work in the emergency departments was total 44 respondents, by percent 6.8% of the respondents had passably knowledge, awareness and 84.1% they were having a good knowledge and awareness and 9.1% the respondents had very good knowledge and awareness. That means almost of respondents have good knowledge and awareness. To find out if an indicator is forming a construct (latent variables) testing the convergent validity of the measurement model with a reflexive indicator assessed based on the correlation between the item score to construct scores were calculated with the help of software Smart PLS. Size reflexive considered valid if the individual has a correlation (loading) to construct (latent variables) to be measured ≥ 0.5 or the value of t-statistics should ≥1.96 (test two tailed) at a significance level of α = 0.05. If one of the indicators has a leading value <0.5,><1.96, then the indicator should be discarded (dropped) because it indicates that the indicators are not good enough to measure the construct in right. The positive influence between general knowledge and awareness against to knowledge about radiation doses can be interpreted that the better general knowledge and awareness, then it will be followed by an increase in their knowledge about radiation doses. And vice versa, the worse general knowledge and awareness, then this will decrease their knowledge about radiation doses too. Conclusion: The present study has illustrated that the level of awareness and knowledge physicians who deal with ionizing radiation in CT scan units are adequate overall. There is a good influence between the diligence in applying the principles of guidance and rules stipulated by the nuclear energy in Indonesia by physicians to adjust the use of CT in the emergency department, the majority of participants who have a good awareness & knowledge, there are some of them do not have enough knowledge.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
Clinicians, Leveraging AI expertise, Understanding the
Regulatory Framework
• Clinical interpretability guiding the adoption of AI-first
differential diagnoses
• Disparity in data literacy affecting the communication of
AI among healthcare scientists
• Regulatory challenges impeding the penetration of AI
into clinical practice - from research to policymakers
Frank J. Rybicki MD, PhD, Professor and Chair, Department
of Radiology, UNIVERSITY OF OTTAWA FACULTY OF
MEDICINE
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Breast cancer detection from histopathological images is done using deep learning and transfer learning techniques. Image processing is done for better accuracy. CNN and DenseNet-121 algorithms are used. 90.9 % accuracy is achieved using CNN and 88% accuracy is achieved using Transfer learning.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
The Complementary Roles of Computer-Aided Diagnosis and Quantitative Image A...Carestream
This presentation from RSNA explains how their similarities and differences have an impact on assessment, quality assurance and training in radiography. Read the blog at http://www.carestream.com/blog/2016/06/07/differences-between-computer-aided-diagnosis-and-quantitative-image-analysis/
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Federated Learning (FL) is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, the panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.
MedicalResearch.com: Medical Research Exclusive Interviews December 14 2014Marie Benz MD FAAD
MedicalResearch.com features exclusive interviews with medical researchers from major and specialty medical research and health care journals and meetings.
Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN Convolutional Neural Network based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77 85 . gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use. Anagha Jayakumar | Mehtab Mehdi "Brain Tumor Detection using Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38105.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38105/brain-tumor-detection-using-neural-network/anagha-jayakumar
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
Study on Physicians Request for Computed Tomography Examinations for Patients...IRJESJOURNAL
Background and objectives: There is a lot controversy about the use of Computed tomography (CT) for patients with minor head injury. We aimed to determine the practice of guiding rules for the safety of radiation and increasing awareness of physicians about risks of ionizing radiation and find out the reasons of emergency doctors for sending head injury patients to CT scan exams. Materials and Methods: A descriptive questionnaire in the Emergency Department (ED) based study was performed to assess physicians' knowledge of radiation doses received from radiological treatments and knowledge about Clinic Decision Support rules (CDS). The questionnaire consisted of 26 questions distributed to physicians working in the emergency department in six hospitals in East Java. Finally, the data collected have been analyzed by some tests using SPSS version 15 and Smart PLS. Results: In this study 44 participants had taken part. The percentage of general knowledge and awareness that shows the response of people who work in the emergency departments was total 44 respondents, by percent 6.8% of the respondents had passably knowledge, awareness and 84.1% they were having a good knowledge and awareness and 9.1% the respondents had very good knowledge and awareness. That means almost of respondents have good knowledge and awareness. To find out if an indicator is forming a construct (latent variables) testing the convergent validity of the measurement model with a reflexive indicator assessed based on the correlation between the item score to construct scores were calculated with the help of software Smart PLS. Size reflexive considered valid if the individual has a correlation (loading) to construct (latent variables) to be measured ≥ 0.5 or the value of t-statistics should ≥1.96 (test two tailed) at a significance level of α = 0.05. If one of the indicators has a leading value <0.5,><1.96, then the indicator should be discarded (dropped) because it indicates that the indicators are not good enough to measure the construct in right. The positive influence between general knowledge and awareness against to knowledge about radiation doses can be interpreted that the better general knowledge and awareness, then it will be followed by an increase in their knowledge about radiation doses. And vice versa, the worse general knowledge and awareness, then this will decrease their knowledge about radiation doses too. Conclusion: The present study has illustrated that the level of awareness and knowledge physicians who deal with ionizing radiation in CT scan units are adequate overall. There is a good influence between the diligence in applying the principles of guidance and rules stipulated by the nuclear energy in Indonesia by physicians to adjust the use of CT in the emergency department, the majority of participants who have a good awareness & knowledge, there are some of them do not have enough knowledge.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
Framework for comprehensive enhancement of brain tumor images with single-win...IJECEIAES
Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.
Clinicians, Leveraging AI expertise, Understanding the
Regulatory Framework
• Clinical interpretability guiding the adoption of AI-first
differential diagnoses
• Disparity in data literacy affecting the communication of
AI among healthcare scientists
• Regulatory challenges impeding the penetration of AI
into clinical practice - from research to policymakers
Frank J. Rybicki MD, PhD, Professor and Chair, Department
of Radiology, UNIVERSITY OF OTTAWA FACULTY OF
MEDICINE
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Breast cancer detection from histopathological images is done using deep learning and transfer learning techniques. Image processing is done for better accuracy. CNN and DenseNet-121 algorithms are used. 90.9 % accuracy is achieved using CNN and 88% accuracy is achieved using Transfer learning.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
The Complementary Roles of Computer-Aided Diagnosis and Quantitative Image A...Carestream
This presentation from RSNA explains how their similarities and differences have an impact on assessment, quality assurance and training in radiography. Read the blog at http://www.carestream.com/blog/2016/06/07/differences-between-computer-aided-diagnosis-and-quantitative-image-analysis/
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Federated Learning (FL) is a learning paradigm that enables collaborative learning without centralizing datasets. In this webinar, NVIDIA present the concept of FL and discuss how it can help overcome some of the barriers seen in the development of AI-based solutions for pharma, genomics and healthcare. Following the presentation, the panel debate on other elements that could drive the adoption of digital approaches more widely and help answer currently intractable science and business questions.
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
An April 2023 presentation to the AMIA working group on natural language processing. The talk focuses on three current trends in NLP and how they apply in healthcare: Large language models, No-code, and Responsible AI.
tranSMART Community Meeting 5-7 Nov 13 - Session 2: Creating a Comprehensive ...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 2: Creating a Comprehensive Clinical and 'Omics Information Commons on Autism
Paul Avillach, Harvard University
지난주말에 있었던 제 4회 대한신경집중치료학회 편집위원회 워크샵에서 발표했던 내용중에 발췌한 것입니다. 원래 제목은 "인공지능 관련 연구: 논문 작성과 심사에 관한 요령" 입니다. 최근에 deep learning in medical imaging으로 2편의 리뷰와 논문 1편, CADD 논문, 앙상블 논문 1편이 되면서 요청이 온것 같습니다.부족한 제가 하기 어려운 주제를 맡았는데, 혹시 도움이 되실 분이 있으면 도움을 되시라고 올려드립니다. 결론은 인공지능 연구라고 특별히 다르지는 않지만, 공학 연구와 의학연구가 다르고, 인공지능 특성을 잘 이해해야 한다 정도 될것 같습니다. (상당부분 저희병원 박성호 교수님의 radiology 논문 Methodology for Evaluation of Clinical Performance and Impact of Artificial Intelligence Technology for Medical Diagnosis and Prediction을 참고했습니다.)
Basics of Data Analysis in BioinformaticsElena Sügis
Presentation gives introduction to the Basics of Data Analysis in Bioinformatics.
The following topics are covered:
Data acquisition
Data summary(selecting the needed column/rows from the file and showing basic descriptive statistics)
Preprocessing (missing values imputation, data normalization, etc.)
Principal Component Analysis
Data Clustering and cluster annotation (k-means, hierarchical)
Cluster annotations
In this talk, we present our work on developing large-scale text mining and machine learning tools as well as their uses in real-world applications in PubMed search, biocuration and healthcare (medical image analysis).
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.
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
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
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.
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
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.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
1. How deep learning reshapes
medicine
Hongyoon Choi
Department of Nuclear Medicine, Seoul National University Hospital
2. Current
Finding
Hypermetabolic solid mass in the RLL (8.3), suggesting
lung cancer
Small LNs in mediastinal 4R, 7 without hypermetabolism.
Otherwise, no abnormal hypermetabolic lesion
suggesting metastasis.
4. Our Future?
Vinyals, Oriol, et al. Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition. 2015.
Vision
Deep CNN
Language
Generating
RNN
“A group of people shopping at
an outdoor market.
There are many vegetables at
the fruit stands.”
Demo: http://vqa.cloudcv.org/
5. CONTENTS
INTRODUCTION
Era of Medical Big Data
DEEP LEARNING IN
MEDICINE
Brief overview of Deep learning
Real application to medicine
PERSPECTIVES
What to solve
New roles as a physician
14. INTRODUCTION
ROC curve
- better than dermatologists
Esteva, Andre, et al. Nature 2017
Computer tech papers invade to Nature/Cell/Science & NEJM/Lancet/JAMA
15. INTRODUCTION
Diabetic Retinopathy
Better or equivalent
to ophthalmologists
Normal DM
Gulshan, Varun, et al. JAMA 2016
ChestXnet
Equivalent/Superior to radiologists (?)
Pranav Rajpurkar, … Andrew Ng, 2017. Arxiv
16. INTRODUCTION
FDA approve a device for diagnosing
diabetic retinopathy (2018.4)
AI-aided system (CT angiography
for large vessel occlusion)
Year of AI invasion to clinic
FDA approve OsteoDetect(2018.5)
19. 3 min overview of deep learning
• Deep learning: y=f(x) where y: label. x: data
f cat
20. 3 min overview of deep learning
• Conventional Perceptron
Lesion size
Circularity
Hounsfield unit
x1
x2
x3
Y
w1
w2
w3
b0
Activation function
Output = 1 if Y >0
Output = 0 if Y <0
Output
1: Malignancy
0: Benign
Find optimized W for minimized error
21. 3 min overview of deep learning
• Limitation in previous perceptron
0 1 1 3 5 7 8 8
0 0 0 1 3 3 5 3
0 0 1 2 4 7 7 1
0 0 2 3 8 5 7 6
2 5 8 8 8 4 9 5
0 0 8 8 6 4 2 3
128x128
=16,384
Output
- Require good manual features
- Raw data Too big.
- More layers? Difficulty in learning
22. 3 min overview of deep learning
• MLP to Deep learning
- Require good manual features
- Raw data Too big.
- More layers? Difficulty in learning
• Automatic feature extraction from raw data
CNN and RNN
• New methods for deep layer training
New activation function & Stochastic gradient descent
• Methods for reducing overfitting
23. 3 min overview of deep learning
• Convolutional Neural Network
• Skim locally, instead of look all things
24. 3 min overview of deep learning
identify line / some texture
identify head lights and wheels
identify Car!
Hierarchical Recogntion
25. 3 min overview of deep learning
• Convolutional Neural Network
26. 3 min overview of deep learning
• Convolutional Neural Network
ImageNet Challenge Results
28.2%
2010
25.8%
2011
16.4%
2012
Shallow model
AlexNet
11.7%
2013
6.7%
2014
3.57%
2015
GoogleNet
ResNet
8-layers
22-layers
152-layers
27. 3 min overview of deep learning
• Recurrent Neural Network
28. 3 min overview of deep learning
• More modules to train deep layers
Problem of Vanishing Gradient
Solved by nonlinearity function
Sigmoid ReLU , tanh, ELU, Leaky ReLU
Sigmoid
ReLU
29. 3 min overview of deep learning
• More modules to reduce overfitting
Problem of overfitting
Apple
30. 3 min overview of deep learning
• More modules to reduce overfitting
Dropout
31. Overview of deep learning
• Current Concept of Deep learning
Deep layered
neural network
Output
+
Data type-specific
layers
Convolution
Recurrent
Modification for
training
+
ReLU activation
SGD training
Dropout
Batch normalization
Variable Loss
…
33. Deep learning in Medicine
DL for medical imaging:
Supervised learning using CNN
f cat
f: CNN
f Lung cancer
Simple application of CNN for diagnosis
34. Deep learning in Medicine
Diabetic Retinopathy
Better or equivalent
to ophthalmologists
Normal DM
Gulshan, Varun, et al. JAMA 2016
ChestXnet
Equivalent/Superior to radiologists (?)
Pranav Rajpurkar, … Andrew Ng, 2017. Arxiv
DL for medical imaging:
Supervised learning using CNN
35. Deep learning in Medicine
DL for medical imaging:
Supervised learning using CNN
AD & NC
MCI-converter & non-converter
FDG and amyloid PET to predict future cognitive decline
Choi H and Jin KH KSNM 2016;
Behav Brain Res 2018
36. Deep learning in Medicine
DL for medical imaging:
Supervised learning using CNN
FDG and amyloid PET to predict future cognitive decline
Accuracy
AD vs. NC
96.0% 85.4% 80.7%
Deep CNN FDG quantification AV45 quantification
Accuracy
MCI conversion
84.2% 75.4% 80.1%84.2%
Deep CNN-based
82%
Moraldi et al. 2016
(MRI+Clinical Variables)
78%
Zhang et al. 2013
(FDG, MRI+Clinical)
Feature Extraction + Machine learning
72%
Shaffer et al. 2013
(FDG, MRI, CSFl)
37. Deep learning in Medicine
Output score measured by
baseline PET
& 3-year cognitive score
f p(Alzheimer|X)
Direct biomarker
A single parameter
Choi H and Jin KH KSNM 2016;
Behav Brain Res 2018
38. Deep learning in Medicine
https://adfdgpet.appspot.com
Online Demo
Input file
Web application
Output: likelihood for AD
& predicted cognitive score
Output:
Cognitive dysfunction-related map
p(Alzheimer|X)
39. Deep learning in Medicine
SWEDD:
Clinically PD,
Image normal
Expert1 Expert2
Conventional
quantification
SWEDD
Deep learning-
abnormal
2-year follow-up
80%
Abnormal
Deep learning : Redefine SWEDD diagnosis
Choi, H., … ,Lee. D.S. NeuroImage: Clinical 2017
Dopamine transporter imaging for Parkinson’s disease diagnosis
3D CNN model
normal Parkinson
40. Deep learning in Medicine
Laborious Work Replaced by DL:
Segmentation
Choi, H., & Jin, K. H. J Neurosci Methods 2016 de Brebisson, et al. CVPR 2015.
41. Deep learning in Medicine
Laborious Work Replaced by DL:
Detection
Liu Y, et al. Arxiv 2017
42. Deep learning in Medicine
Enhance Image Acquisition & Quality
Dahl et al. Arxiv 2017
Standard dose
1/200 Dose 1/200 Dose + CNN
43. Deep learning in Medicine
Image Generation
Cat
Cat
Common deep learning model Generative model
z = f(x)
where x: data, z: discriminative features
f: classifier model
x = g(z)
where x: data, z: latent
g: generation function
44. Deep learning in Medicine
Generative Adversarial Network
z~N(0,1)
G:
generator
Karras T, et al. Arxiv 2017
G:
generator
Isola P, et al. arxiv, 2016.
45. Generative Adversarial Network
Structural MR generation from PET
Florbetapir PET
Generator:
U-net
Skip connection
Generated MR
PETandgeneratedMRPETandrealMR
Discriminator Real or Fake
Generative Adversarial Networks
for MR generation
z G(z)
z & G(z)
z & x
Choi H and Lee DS, J Nucl Med 2017.
Deep learning in Medicine
RealMRI
Generated
MRI
18F-Florbetapir
PET
46. Deep learning in Medicine
PET-based normalization
SUVR
measurement
MRI-based normalization
SUVR
measurement
MR generation
Generated
MRI-based normalization
transformation apply to PET
SUVR
measurement
Choi H and Lee DS, J Nucl Med 2017.
Generated-MRbased
quantification
PreviousPET-based
quantification
Gold standard
Gold standard
47. Deep learning in Medicine
Conditional Generation
Antipov G, Arxiv 2017
49. Choi H,… Lee DS. Biorxiv 2017
Estimating normal population distribution
Deep learning in Medicine
50. Deep learning in Medicine
• Individual vs population
– Aging of individual brain : Comparing with virtual population
Population distribution of brain
metabolism at each age by iterative
generating from the VAE model
Generator
Latent
features
N(0,1) Random sampling
from normal distribution
+ Age
Age
50
55
60
65
70
75
• Evaluating individual brain’s aging
compared with general population
• Define ‘pathologic aging’
Choi H,… Lee DS. Biorxiv 2017
53. Practical Issues
– Various purposes
• More than simple diagnosis: Prognosis, Disease
status monitor, Response prediction.
– Ambiguous ground-truth
• Diagnosis is not a simple classification
Gap between Tech-initiated DL and Clinician’s need
54. Practical Issues
Clinical diagnosis:
Spectrum instead of
a clear-cut classification
Blood pressure = a type of biomarker
Cardiovascular
eventrisk
140/90
HypertensionNormal
Blood Pressure
(one-dimensional)
Quantitative biomarker
reflecting prognostic outcome
Direct
Most diseases…
Multiple domain and high-dimensional
Not rely on single measurement
55. Practical Issues
SUVmax
A value
maximize
information
of whole voxels
to represent
patient’s prognosis
What we expect: Deep learning as a ‘best parameter’ extractor
from high-dimensional data
Simple Diagnosis << Biomarker
57. Practical Issues
Where to get
Hospital
• Best source
• Practical model validation: Real world
problems
• Ethical issues and relatively small data size
Public database
• Easy to get
• Red ocean
• Limitation in real-world validation
59. Practical Issues
Data Size
– Does deep learning require big data?
– Always more than 100,000 images?
Answer : No
Answer : Yes
60. Practical Issues
Data Size
– Voxel-based training:
Segmentation/Superresolution etc.
– Image generation (Pix2Pix)
:~100 3d volumes
– Image augmentation
: rotation/flipping
(but, cautious)
– Task and complexity dependent:
AD vs normal was trained by ~300 cases.
61. Practical Issues
Image labels
– Unlabeled data >> Labeled data
• e.g. all FDG PET/CT >> Baseline breast cancer FDG PET/CT
– Unbalanced label
• e.g. Lymphoma PET >> Adrenocortical carcinoma PET
Importance of unsupervised & semi-
supervised learning
62. Practical Issues
Tumor metabolism
estimated by
FDG PET image
Gene networks
Gene expression data with
FDG PET image
~ 40 pairs
Gene expression data alone
~ 1000 cases
Image labels
Example of semisupervised learning in practical problem
Choi H and Na KJ. Theranostics 2018
63. Practical Issues
Expressiondata
Tumor metabolism
(maximum SUV)
Encoder
Decoder
HiddenFeatures
626 64 1
Supervised Training
(n = 20)
Unsupervised Training
(n = 226)
Dimensions
Example of semisupervised learning in practical problem
Semisupervised learning
- Supervised training
combined with autoencoder
Choi H and Na KJ. Theranostics 2018
64. Practical Issues
At report…
- Lung cancer cannot be excluded.
Rec> “Clinical correlation is recommended”
Issues of Uncertainty
Suggestive of cancer
Probably, cancer
Possibly, cancer
Cancer cannot be excluded
Recommend
further exam
Deep learning trained
by Natural Images
“Cat”
65. Practical Issues
Data distribution
Dataset for model training/validation
Alzheimer Normal
Real data
at Hospital
Normal
Alzheimer
FTD
DLB
PSP
Depression
Real data
at Community Normal
• Will DL model work at real clinic?
• How to deal with rare/unseen disease?
Aged person with
subjective cognitive decline
66. Perspectives as a Physician
Medicine is too complex
Problem of responsibility
Too many empirical things
Didn’t you see AlphaGo?
Domination is already started
At least, many doctors will lose
their job
67. Perspectives as a Physician
Tsunami in medical fields
Deep Learning
Big Data
68. Perspectives as a Physician
• Gaps between Tech. & Hosp.
• Become an expert of data
• Roles will be changed
– Not just replace MD’s role
– Laborious things replaced by AI
– New information produce new jobs
69. Perspectives as a Physician
• Roles will be changed
– Laborious things replaced by AI
– New information produce new jobs
• Performed by AI ~1 sec.
• New information for medical decision
(Cortical thickness, Tumor volume,
etc
as a clinical routine)
70. Perspectives as a Physician
Disruptive Innovation: Raw medical & healthcare data
Diet + Previous Glucose Level
Future Glucose Level &
Scheduling Insulin
Sugar.iq from Medtronic
(FDA approved 2016.9)
71. Perspectives as a Physician
Disruptive Innovation: Raw medical & healthcare data
72. Perspectives as a Physician
Disruptive Innovation: Raw medical & healthcare data
HTC DeepQ Tricoder
Predicting PVC from daily EKG
Diagnosis of otitis media
73. Perspectives as a Physician
• Accelerating changes to daily healthcare
from hospital-based health
Deep learning facilitates left-shifting
74. Perspectives as a Physician
Future medical decision
ConcatenatingFeatures
• Diagnosis
• Management Plan
AbnormalNormal
Integrated biomarker based on DL
RiskatDeath
Human
Cannot Do!
75. Perspectives as a Physician
Future medical decision
Previous classification
Single target
Based on some receptors
Breast cancer
classification
Current classification
Multiple targets
Based on ~50 transcripts
76. Perspectives as a Physician
Future medical decision
Breast cancer
classification
Genome
Phenome
Proteome
Imaging
Metabolome
Biosensor
Unsupervised learning
Integrative classification
Future classification
Multiomics targets
Based on unsupervised learning
77. Perspectives as a Physician
Empirical
-based
Evidence
-based
Data
-driven
“약을 써보니 낫더라
“RCT를 해보니
이 약은 효과가
유의하게 있다
“모든 이 사람의 활동 데이터,
Omics 데이터, 영상 정보 등을
통합하여 볼 때 이 약이 가장
적합할 것이다
Deep Learning
Editor's Notes
강의순서
Intro
Intro of Intro: 조직학 + 최근구글비디오 – 괴리.
누가 biomedical field를 점령할 것인가.
Data의 시대
각론
Deep learning이 어떤 역할 : Image data augmentation + Image diagnosis
Omics data 등.
연속측정가능, raw data, easily acquisible data로 부터 의미 추출하기
매우 낮은 수준의 데이터를 어떻게 활용할 것인가. EMR data부터 기본 monitor장비에서의 데이터, 나아가서 wearable device. 파괴적 혁신을 일으키는 deep learning
Biomarker와 data 통합에 대해.
미래의료와 딥러닝관련
앞으로 의사가 해야할 일?
무엇의 전문가가 될 것인가. 의사의 role.
SO, I’ll briefly introduce current trends in medical fields in terms of data a science. I can darely say your role as an expert in data will substantially change medical environment. Next, I’ll introduce my recent researches and related researches related to deep learning-based biomarker. And, I’ll share some practical issues in deep learning and perspectives.
Recently, many doctors’ intrests were totally changed. When we get some meeting with doctors, one of the common themes of chat is Artificial intelligence. I think now, it’s era of medical data scientists.
Health care market is really big market. In US, healthcare market occupies 15% of GDP and in Korea, around 8% of GDP. Then, who gets this money? And who is the big brother in this market?. So far, in this market, ruler was pharmaceutical companies. They target a specific symptoms, specific disease and even more, specific molecules. But all of things are changed. Biomedical data are markedly increased and role of the analysis of these data is increased. Thus, tech companies are entering healthcare markets and dominating the market.
For example, a representative AI company, deepmind already launches deepmind health and they have tried to develop medical AI applications. Google and google’s subcompanies such as verily have developed various biotechnologies based on IT. IBM Watson is another famous AI applciations in hospital. Apple also develops various healthcare devices such as apple watch combined with electrocardiogram. Even more, healthcare startups are rapidly increasing and some other hardware companies such as Samsung also entering into health care market.
The core is flood of data. Previously, researches only focused on single molecule, single gene. What the role of a specific gene is. But, today, its thousand dollar genome era. Only 1000$ is required for analyzing personal genome.
Furthermore, due to wwearble technology, medical data are collected from common life. Not from hospital. Iphone combined EKG was approved by FDA which enables routine checkup for general people. Continous blood glucose monitoring system can generate 24 hr, 365 days blood glucose data.
Paroxysmal Supraventricular tachycardia
This change was based on two factors. Increased data and appropriate algorith, deep learning. These synergistic integration of these two factors induce tsumani in medical fields which is inevitable.
Besides, AI application to medical fields is also markedly increasing. Global market of AI industry is rapidly grown and will occupy around 25 billion dollars. And among various subfields in medical area, the first target is image analysis. As boom of deep learning was started from ImageNet challenge and many innovative deep learning models have been developed for image recognition and process, the first target of AI in medicine is medical imaging.
This AI doctor seems to be becoming reality in recent research.. This nature paper was published by Stanford group. They used more than hundred thousands of skin lesion images and deep learning model, Inception, GoogleNet, to diagnose skin cancer. The performance was better than dermatologists and they develops prototype which can be embedded in smartphone app.
For discriminating fundoscopic image, a job of ophthalmologists, DL model shows better performance for discriminating diabetic retinopathy. Recently another big guy of AI, Andrew Ng introduce Chest Xnet which interpret chest X-ray image equivalent to radiologists.
For discriminating fundoscopic image, a job of ophthalmologists, DL model shows better performance for discriminating diabetic retinopathy. Recently another big guy of AI, Andrew Ng introduce Chest Xnet which interpret chest X-ray image equivalent to radiologists.
Then, I’ll lose my job? My reading will be totally replaced by AI? I think DL application to medical imaging should not only focus on replacement of doctor. Deep learning can play important role in medicine and innovate and progress by solving clinical unmet needs. So, I want to introduce what clinician want to DL. And because of unique characteristic of medical imaging data, some practical issues in DL application exist. So I’ll show you in next part.
So, the input of the model was both PET volumes and the output was probability of Alzheimer. This probability score can be used for predicting cognitive outcome in MCI subjects as well as discriminating AD. The y-axis represents cognitive score and high CDR means poor cognitive function. The output of the model of baseline image was correlated with future cognitive score changes.
This 3D CNN model was embedded into web application. If you have some FDG brain PET images, entering the image can produce the probability of Alzheimer and the cognitive dysfunction-related map like this.
So, from supervised learning, I can develop a deep CNN model that differentiate PD from normal with better performance than me, and my colleagues. Furthermore ,a specific disease entity exists in PD, scans without evidence of dopaminergic deficit. This entitiy is a Parkinson’s disease but don’t show abnormality in Dopmaine imaging. This abnormality has been determined by human reading, but from our DL model, we found that some of SWEDD patients were not really SWEDD as they have imaging abnormality.
This process was facilitated by deep learning segmentation. This was my study, one of the first study that specific brain region segmentation using deep learning.
This process was facilitated by deep learning segmentation. This was my study, one of the first study that specific brain region segmentation using deep learning.
This process was facilitated by deep learning segmentation. This was my study, one of the first study that specific brain region segmentation using deep learning.
I can summarize in a word what clinician want for DL is a biomarker. Technologist-initiated DL model focuses on above or human level image interpretation. Thus, sort of arxiv papers chase better performance in disease lesion detection, segmentation and classification. But really, clinical want to know whether the subject die or not and if then, when will die. And what the proper treatment is. This answer is directly related to biomarker.
This gap between technicians and clinicians are started from these issues. Medical imaging has various purposes, not only for simple diagnosis. Clinician acquire medical imaging and exams to know patients’ outcome and to monitor disease status during treatment. Furthermore, diagnosis is not clear-cut as ImageNet challenge classification.
Clinical diagnosis is spectrum instead of clear cut classes. For example, hypertension is defined by higher systolic blood pressure 140 or higher diastolic blood pressure 90. But Blood pressure is a spectrum. Just a criteria was used to determine initiation of antihypertensive treatment. Because this point is related to increased risk of cardiovascular disorders. Clincian want a parameter, blood pressure, instead of definitve diagnosis of hypertension. That is, blood pressure is a one dimensional parameter, quantitative biomarker that reflects prognostic outcome in terms of cardiovascular disorders.
However, many disorders are defined by multiple domains and highdimensional data instead of single parameter such as blood pressure.
For example, brain diseases are diagnosed by symptoms, brain imagng, lab test and drug responses. Doctor makes decision qualitatively and empirically by considering patients’ predicted outcome. What clinician want to deep learning or AI is ‘summarized biomarker’ which reflects these high-dimensional and multimodal data. Thus, instead of simple diagnosis, DL shoud focus on generating a simple feature score, biomarker, which reflect patients’ outcome like blood pressure.
