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Jost B. Jonas, MD is a comprehensive ophthalmologist and clinical scientist and is Chairman of the Department of Ophthalmology of the Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg/Germany.
He became member of the German Academy of Science Leopoldina, the Glaucoma Society of the International Congress of Ophthalmology, and the Macula Society and Retina Society, and provisional member of the Academia Ophthalmologica Internationalis. He is Honorary Member of the French Ophthalmological Society and Asia-Pacific Vitreoretinal Society and Fellow of the Association of Research in Vision and Ophthalmology ARVO. He has received the Glaucoma Award of the German Ophthalmologic Society, the Junior and Senior Award of the American Academy of Ophthalmology, the Senior Clinical Scientist Award of the World Glaucoma Association, the Prof. Robert-Ritch Glaucoma Award, the International Gold Awards of the Chinese Ophthalmologic Society, and the Senior Award of the Asian Pacific Academy of Ophthalmology
He has research interests in the intravitreal application of medication as treatment of intraocular edematous, proliferative and neovascular diseases; the intravitreal cell-based (drug) therapy; the homologous intravitreal bone-marrow transplantation; the retinal microglial cell system; the contact lens associated ophthalmodynamometry for measurement of the retinal arterial and venous blood pressure and cerebrospinal fluid pressure; the morphologic diagnosis of optic nerve diseases including the glaucomas; the association between the cerebrospinal fluid pressure and ocular disorders; population-based studies (Beijing Eye Study 2001, 2006, 2011; Beijing Public Health Care Study; Beijing Pediatric Eye Study; Beijing Children Eye Study; Beijing High-School Students Study; Shandong Children Eye Study; Gobi Desert Children Eye Study; Asymptomatic Polyvascular Abnormalities in Community (APAC) Study; Kailuan Study; Ningxia High Myopia Study; Central India Eye and Medical Study; Central India Children Eye Study; Ufa Eye and Medical Study); and in the process of emmetropization and myopization.
Moderators: Lily PENG & Daniel TING
Moderators: Robert CHANG & Gavin TAN
Eye care services should be embedded in the primary health care system at the community level in order to ensure them to be sustainable, affordable, equitable, and comprehensive in order to achieve the Universal Eye Health. To attain the continuum of care for eye diseases, it is important to establish the capacity of the key informants, which refer to already existing health care human resources in the community including the Village Health Workers.
The rapid proliferation of teleophthalmology has plenty of benefits on this matter because it may extend the coverage of proper eye health services to the areas where there are barriers to deliver them through empowering the key informants’ capacity to provide eye health services. From numerous cases of pilot studies, teleophthalmology has demonstrated results beyond expectation in the conventional approach of public eye health, which yielded quite optimistic forecast of the field.
However, there are scarce number of cases regarding the large-scale implementations of teleophthalmology at the moment. Moreover, teleophthalmology is no exception for the drawbacks of the pilot studies so called ‘pilotitis.’ Therefore, this presentation will be discussing about the introduction of sharing , monitoring and evaluation tools used for digital health interventions or mobile health to the teleophthalmology to provide guidance for better direction.
In this talk, our recent work on AI applications on tele-ophthalmolgy will be shared. In these applications, AI not only can help clinicians to see faster and better, but also can see more things beyond clinician eyes to provide more clinical value by connecting dots which may not be easily picked up by clinicians. Some challenges and potential future areas of AI-driven healthcare applications will also be discussed.
In 2012, together with technology company Healgoo Interactive Med Tech, Professor Mingguang He invented EyeGrader, a deep learning-based grading system for the detection of referable diabetic retinopathy, glaucoma, late age-related macular degeneration and cataract from standard color fundus photographs. To promote the real-world application of this technology, his ongoing research has led to the development of a fully functioning, web-based and offline clinician interface, with integrated, automated diagnosis and immediate output of a single page grading report. This system has been widely adopted in the Lifeline Express DR screening program in China, and with support from the BUPA Health Foundation and Medical Research Future Fund, his current research in Australia focuses on assessing the impact, end-user acceptance and cost-effectiveness of this technology as a novel point-of-care screening model for use within endocrinology and primary care settings. This solution offers significant potential benefits including an increased efficiency, accessibility and affordability of eye disease screening programmes.
Prof. Mingguang He is currently Professor of Ophthalmic Epidemiology at the University of Melbourne and Centre for Eye Research Australia, Director of WHO Collaborating Centre for Prevention of Blindness (Australia). He is a Fellow of Royal Australian and New Zealand College of Ophthalmologists. He was the former Associate Director and Professor of Ophthalmology in the Zhongshan Ophthalmic Center, Sun Yat-sen University in Guangzhou.
He graduated from Sun Yat-sen University of Medical Sciences and got degrees of Doctor of Medicine and Doctor of Philosophy. He received his research training in Johns Hopkins University (MPH) and University College London (MSc. PhD).
His research interest includes clinical and epidemiological research, randomized clinical trial, twin study, imaging technology and big data research. He ran the first population-based study on glaucoma in mainland China and further epidemiological studies on refractive error, presbyopia and other eye diseases. He established several large cohort studies including the Guangzhou Government Servant Eye Study, Chinese High Myopia Registry and Guangzhou Twin Eye Registry. He ran a clinical trial on prophylaxis of angle closure in Chinese people with 4 years follow-up. From 2009 to 2013 he ran a clinical trial to investigate the impact of outdoor intervention on the control of myopia and the results was published in the American Medical Association (JAMA) in 2015. During the recent years, he has been dedicated in inventing and applying devices that facilitate the diagnosis and surgery of eye disease, like fundus photo auto-grading system with Artificial Intelligence and Deep learning technology, visual accuracy self-assessment.
He has published nearly 280 papers in the international peer-reviewed journals including JAMA, Ophthalmology, British Journal of Ophthalmology, Invest Ophthalmology and Vision Science and some important book chapters, with more than 9000 citations. The current H-index is 47. He has been awarded more than 20 grants from Chinese, Australia and other international funding bodies and is currently holding 17 patents. He has given more than 90 invited lectures at Asia regional and international conferences.
He serves as editorial board member for several important journals, including the Ophthalmology, the Top 1 ophthalmology journal, and deputy editor of Eye Science. He serves as President of Asia Pacific Tele-Ophthalmology Society (APTOS), Deputy Secretary-General of Asia-Pacific Academy of Ophthalmology(APAO), Director of Preventive Ophthalmology Panel, Chinese Ophthalmological Society, and Country Director of Helen Keller International. He has received several awards including a distinguished young scholar award from the National Natural Science Foundation of China (2011) and Holmes Lecture Award from the Asia-Pacific Academy of Ophthalmology (2015).
Prof. Ningli Wang has engaged in clinical and scientific research in ophthalmology for 34 years, and performed more than 20,000 surgeries. He currently serves as a consultant expert in Central Health Care Committee. The main areas of his works are: the pathogenesis and diagnosis and treatment of glaucoma.
He is the Director of Beijing Tongren Eye Center (Key Discipline of the Ministry of Education and Clinical Key Specialty of National Health and Family Planning Commission), and Director of the National Research Center for Clinical Diagnosis and Treatment Engineering Technology. He served as primary investigator (PI) of 11 major projects, including National High-tech R&D Program (863 Program), Key Program of National Natural Science Foundation of China (NSFC), NSFC Projects of International Cooperation and Exchange, Major Project of the Ministry of Science and Technology of China. Besides that, he has led 2 national major blindness prevention projects. Prof. Wang has by now served as supervisor for 10 postdoctoral students and 52 doctoral students.
He has published 429 peer-review papers as the first author or corresponding author, including 167 SCI articles, with 5388 citations in total and 4765 citations by others. Among his publications, 25 papers are published on articles (first author or corresponding author) the top two international ophthalmology journals; 4 papers are published on Nature Genetics and 1 paper on Nature Communication. In terms of the number of articles published in the glaucoma field, he ranks the first in China and top 10 worldwide. He is the chief-editor of 26 textbooks and monographs on ophthalmology and 12 standard specifications, and has been authorized 20 patents. As PI, he was awarded 2 National Science and Technology Progress Second Class Awards and 4 Provincial Science and Technology Progress First Class Awards.
In brief, Dr Mo Dirani is a senior research fellow, entrepreneur and MD of plano, an innovative application that manages smart device use and myopia in children, all while playing in the background of phones and tablets. Dr Dirani completed his PhD at the age of 26, after producing the world’s largest twin study on myopia. He is an internationally renowned researcher, known for his public health research into myopia and other common eye conditions. He has led several population based studies into the prevalence and causes of eye disease in children and adult populations, both in Singapore and Australia. Dr Dirani has published over 100 peer-reviewed manuscripts published in prestigious ophthalmology journals, contributed to several government commissioned reports, and has had an estimated 200 media stories published on his work. He has also been an invited speaker at many local and international eye conferences. Dr Dirani has made the move to Singapore and is very excited to get amongst local entrepreneurs and key stakeholders to ultimately translate known research findings into high impact solutions.
Tele-medicine, or tele-health, refers to the practice of medicine at a spatial and/or temporal distance by exchanging medical information via electronic communications. Tele-health and artificial intelligence are gaining ground as new innovations shaping the future of medicine. The practice of ophthalmology lends itself to the practice of tele-medicine through its heavy reliance on imaging. With the advancing technology and high-speed connectivity tele-ophthalmology and artificial intelligence are poised to transform ophthalmology into large interconnected systems and to improve the efficiency, quality, outcomes, and accessibility to healthcare, while decreasing costs.
Access to care is problematic in high risk and underserved communities in New York City due to multitude of reasons. In our study published in 2017 (Screening for glaucoma in populations at high risk: The eye screening New York project) we found that 57% of the screened individuals never saw an eye doctor in their lifetime regardless of having insurance. Subsequently we initiated a study to screen for the four-leading cause of blindness using a mobile tele-ophthalmology unit equipped with state of the art devices and staffed with technicians, linked in real-time to a reading center. Our pilot study had a high rate of disease detection when used in high risk communities. This initial experience establishes the feasibility of mobile tele-ophthalmology as a method of facilitating access to care. Furthermore, it highlights the importance of an active blindness prevention program in the context of population management.
Artificial intelligence algorithms using deep learning are showing great promise in medicine. Artificial intelligence algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. The FDA recently approved IDx-Dr AI based diagnostic system for the autonomous detection of diabetic retinopathy. Recently we validated the use of Pegasus Deep Learning System (PDLS) in identifying glaucomatous optic damage on disc photos when compared to a reference standard comprised of expert graders. The PDLS achieved an AUC-ROC of 83% (P<0.05) with sensitivity of 96.1% and specificity of 58.3%. its high sensitivity suggests potential utility for glaucoma screening in settings where specialists are not available, including tele-medicine programs.
Dr. Lama A. Al-Aswad is an ophthalmologist with subspecialty in glaucoma and cataract with a strong interest in disease prevention and population health management. She is an Associate Professor of Ophthalmology at Columbia University, Glaucoma fellowship director, Chair of quality assurance of the Eye Institute and the Director of the Tele-ophthalmology initiative. Dr. Al-Aswad received her medical degree from Damascus University Medical School and a Glaucoma research fellowship from Mass Eye and Ear infirmary Harvard Medical School. She completed her Residency in ophthalmology at SUNY Downstate and her Glaucoma fellowship at UT Memphis. Dr. Al-Aswad is a Board certificated ophthalmologist, the past president of the NY Glaucoma Society and the current president for the Women in Ophthalmology. She is an active member of several professional societies. In 2015, Dr. Al-Aswad was also conferred the degree Master of Public Health from Columbia University, Mailman School of Public Health for her work in healthcare policy and management.
Dr. Al-Aswad’s dedication to science and the scientific method is evident from her list of scientific publications, book chapters, invited articles and a large number of scientific presentations at national and international meetings. Dr. Al-Aswad is a believer in prevention of blindness as evident from her large-scale glaucoma screening project in NYC where she screened more than 8500 individuals for glaucoma. She recently launched the tele-ophthalmology screening project for the four-leading cause of blindness using a mobile tele-ophthalmology unit equipped with state of the art devices and staffed with technicians, linked in real-time to a reading center. Dr. Al-Aswad is currently working on validating artificial intelligence systems in glaucoma and diabetic retinopathy screening as a tool in blindness prevention.
In this presentation we will review the latest development in deep learning and look at their recent applications and future impact on retinal image analysis in tasks such as prognosis and diagnosis as well as other AI applications to healthcare and medical image analysis, and will conclude by providing perspectives on clinical deployments.
Dr. Phil Burlina holds joint faculty positions at the Johns Hopkins University School of Medicine Wilmer Eye Institute, the Malone Center for Healthcare Engineering and the Department of Computer Science. He is a principal scientist with the Johns Hopkins University Intelligent Systems Center at the Applied Physics Laboratory. Dr. Burlina’s research spans several areas of machine intelligence including machine learning, deep learning, machine vision, object detection and recognition, deep reinforcement learning, medical image diagnostics, and addressing problems of making AI work in the wild such as zero/one/adaptive shot learning and unsupervised learning. His interests are in the development AI algorithms that are impactful for problem areas in medicine, robotics, and autonomous navigation.
A total of 5% of newborns have potentially treatable pathology present that can result in vision loss and blindness. Diseases are diverse ranging including retinal detachment, blastomas and many others. We detect abnormalities related to these events in images of the newborn retina and the eye.
Newborn eye screening in newborns can be done with photographic screening in the first 48-72 hours of birth, while the infant is still in the hospital. In fact, we know that fundus hemorrhages are most easily detected in this time period, rapidly dropping off over the next 4 weeks. The same is true for non-hemorrhagic, non-amblyogenic, defined diagnosis vision threatening pathology in 1.5-2.5% of newborns.
For universal screening ~4 million newborns, i.e. 50 million images, must be screened annually in the US. We are deploying an AI system that can perform such screening with high accuracy. Upon detection of abnormality the system alerts the clinic to initiate referral within 1 week after birth, which is comfortably within the effective treatment window to prevent vision loss.
Deep learning architectures were optimized to develop the Pr3Novo™ Classifier to detect abnormality in healthy term newborn retinal images. Deep learning using neural network has been successfully applied to image analysis and has been used to classify adult retinal images for macular degeneration, glaucoma and diabetes. With training set of 5000 scored images we identified than 89% of abnormal images. Retina specialists will curate a larger portion of Pr3vent’s in-house database of ~250,000+ newborn eye examination images to enable significant increase in accuracy. Further work has added the ability to detect abnormality in images of healthy term newborn eyes (anterior segment images). Similar approaches requiring a curated training set of 3000 patient samples with >6,000 curated images have proved effective.
The AI system presented here is scalable and follows a single regulatory pathway to provide nationwide service. It performs at a level comparable to a specialized pediatric opthamologist, nearly eliminates human error and has significant economic and health impact.
Jochen Kumm is the CEO of Pr3vent, a universal screening company. He is a Visiting Scholar in the Department of Ophthalmology at the Stanford University School of Medicine. He is originally geneticist and a mathematician focused on healthcare applications of Deep Learning and AI. He is a sequential founder/advisor of start-ups that operate in Europe, North America and Asia including NextBio, Pathogenica, Veracyte, Pinpoint Science and insightAI, and hold multiple patents in diagnostics and AI.
Educated at Harvard University and Stanford University, he worked at the Department of Statistics at the University of Washington and at the UW Genome Center. At Roche Pharmaceuticals he was – as head of Computational Biology – global technology lead for genomics. Subsequently, he led biomathematics at the Stanford Genome Technology Center for a decade. In collaborations with Harvard, MIT, UCSF, UCSC, the CDC and others, Dr. Kumm has developed tools used by the CDC, biotech, financial institutions and the British Home Office. More recently, he worked for IBM Research and IBM Watson developing large-scale healthcare solution.
Retinopathy of prematurity (ROP) is an important cause of blindness in premature infants throughout the world. Current clinical management consists of screening, decisions for which are typically based on only birth weight and gestational age at birth; diagnosis by ophthalmologist examination, which in some hospitals is triggered by preceding retinal image grading; and treatment with laser photocoagulation or intravitreal injection of anti-vascular endothelial growth factor agents, to prevent progression to retinal detachment. Current ROP screening guidelines, based on studies of high-risk infants and expert opinion, have low specificity for disease requiring treatment. Based upon advances in the understanding of the pathogenesis of ROP, numerous postnatal-weight-gain-based models have been developed to improve the specificity of ROP screening, but these models have been limited by complexity and small development cohorts, which result in model overfitting and resultant decreased sensitivity in validation studies. To overcome these limitations, the postnatal growth and ROP (G-ROP) collaborative study group has recently carried out two large-scale multicenter studies to develop and validate a clinically implementable, birth weight, gestational age, and weight-gain prediction model, which takes the form of modified ROP screening criteria. In this presentation, we will discuss principles of clinical predictive models and demonstrate these principles using the G-ROP Studies and preceding ROP predictive model studies. The story of these models demonstrate how large amounts of detailed clinical data can help to guide clinical practice, not only improving the efficiency of care but also potentially allowing screening practices to be updated in response to changes in care that directly impact the profile of infants at risk for ROP, if data registries contain sufficiently detailed medical information. The models highlight the fundamental importance of dataset size in the development of clinical prediction tools. The G-ROP Studies datasets also can be used to study ophthalmologist practice patterns and produce evidence-based examination schedules. Finally, these approaches can be integrated into a hybrid system, in which predictive modelling is combined with telemedicine to accurately, promptly, and more efficiently identify infants who develop severe ROP and require referral to an ophthalmologist for possible treatment.
Gil Binenbaum MD MSCE is the Richard Shafritz Endowed Chair of Ophthalmology Research and Attending Surgeon in the Division of Ophthalmology at The Children’s Hospital of Philadelphia, and Associate Professor of Ophthalmology at the Perelman School of Medicine of the University of Pennsylvania, in the United States. He completed medical school, residency, fellowship, and graduate studies in clinical epidemiology and biostatistics at these same institutions. One of his primary research interests is retinopathy of prematurity, for which he is Chair of an international, multicenter ROP research group funded by the National Institutes of Health; their goal is to use biomarkers such as postnatal growth to improve prediction of ROP risk and increase the efficiency of ROP screening. Another major research interest is mechanisms and patterns of intraocular injury in pediatric head trauma, the goal of which is to improve the accuracy of the diagnosis of child abuse. Dr. Binenbaum is a research and clinical mentor for students, at all levels of training, from college and medical school to residency and fellowship. His clinical practice specializes in pediatric and adult strabismus surgery, retinopathy of prematurity diagnosis and treatment, and consultative pediatric ophthalmology.
Michael F. Chiang, MD, is Knowles Professor of Ophthalmology & Medical Informatics and Clinical Epidemiology at the Oregon Health & Science University (OHSU) Casey Eye Institute, and is Vice-Chair (Research) in the ophthalmology department. His clinical practice focuses on pediatric ophthalmology and strabismus. He is board-certified in clinical informatics, and is an elected Fellow of the American College of Medical Informatics. His research has been NIH-funded since 2003, and involves applications of telemedicine, clinical information systems, computer-based image analysis, and genotype-phenotype correlation to improve delivery of health care. His group has published over 140 peer-reviewed journal papers. He directs an NIH-funded T32 training program in visual science for graduate students & postdoctoral fellows at OHSU, directs an NIH-funded K12 mentored clinician-scientist program in ophthalmology, and teaches in both the ophthalmology & biomedical informatics departments. Before coming to OHSU in 2010, he spent 9 years at Columbia University, where he was Anne S. Cohen Associate Professor of Ophthalmology & Biomedical Informatics, director of medical student education in ophthalmology, and director of the introductory graduate student course in biomedical informatics.
Dr. Chiang received a B.S. in Electrical Engineering & Biology from Stanford University, and an M.D. from Harvard Medical School & the Harvard-MIT Division of Health Sciences and Technology. He received an M.A. in Biomedical Informatics from Columbia University, where he was an NLM fellow in biomedical informatics. He completed residency and pediatric ophthalmology fellowship training at the Johns Hopkins Wilmer Eye Institute. He is past Chair of the American Academy of Ophthalmology (AAO) Medical Information Technology Committee, Chair of the AAO IRIS Registry Data Analytics Committee, member of the AAO IRIS Registry Executive Committee, and member of the AAO Board of Trustees. He is Associate Editor for the Journal of the American Medical Informatics Association (JAMIA), Associate Editor for the Journal of the American Association for Pediatric Ophthalmology & Strabismus, and serves on the Editorial Boards for Ophthalmology, Ophthalmology Retina, Asia-Pacific Journal of Ophthalmology, and EyeNet. He has received “Top Doctor” awards from Castle Connolly, Best Doctors in America, and Portland Monthly magazine, and has received numerous research and teaching awards.
One of the major areas in ophthalmology where the application of telemedicine and artificial intelligence has been proposed is retinopathy of prematurity (ROP). This talk will discuss challenges in traditional ROP management, ways in which telemedicine has potential to improve the quality and delivery of care by addressing these challenges, and the published evidence to date. We will then discuss challenges in ROP diagnosis, ways in which artificial intelligence methods have potential to make diagnosis more objective and quantitative, and the published evidence to date. This talk will conclude by summarizing how ROP care is evolving because of these technologies.
I’m a non-practicing physician and product manager for a team that works on applying deep learning to medical data, especially medical imaging. Here is some of our team’s recent work in diabetic eye disease (JAMA & TensorFlow Dev Summit) and pathology.
Before Google, I was a product manager at Doximity, the “linkedin” for physicians, and a co-founder of Nano Precision Medical (NPM), a medical device start-up developing a small implantable drug delivery device. I completed my M.D. and Ph.D. in Bioengineering at the University of California, San Francisco and Berkeley. I received my B.S. with honors and distinction in Chemical Engineering from Stanford University.
Deep learning is a family of machine learning techniques in which multiple computational units, organized in layers, work together to model complex systems with high accuracy by learning from examples. Deep convolutional neural network is a specific subtype of deep learning optimized for images. This technique has produced algorithms that can diagnose melanoma, breast cancer lymph node metastases and diabetic retinopathy from medial images with comparable accuracy to human experts. This talk covers work in applying deep learning to retinal imaging for diabetic retinopathy, including recent work in using different reference standards and techniques to improve explainability. It will also cover how retinal images and deep learning can be leverage to make novel predictions such as cardiovascular risk factors.