Developing a Machine-Learning Algorithm to Diagnose Age-Related Macular Degeneration
Monta Vista High School（蒙塔维斯塔高中）；Hanoi-Amsterdam High School（河内-阿姆斯特丹高中）；Churchill Fulshear High School（丘吉尔·福尔瑟高中）；Norfolk Academy（诺福克学院）；John H. Guyer High School（约翰·H·盖尔高中）；Collège Sainte-Anne（圣安妮学院）
Today, more than 12 million people over the age of 40 suffer from ocular diseases. Most commonly, older patients are susceptible to age related macular degeneration, an eye disease that causes blurring of the central vision due to the deterioration of the retina. The former can only be detected through complex and expensive imaging software, markedly a visual field test; this leaves a significant population with untreated eye disease and holds them at risk for complete vision loss. The use of machine learning algorithms has been proposed for treating eye disease. However, the development of these models is limited by a lack of understanding regarding appropriate model and training parameters to maximize model performance. In our study, we address these points by generating 6 models, each with a learning rate of 1 * 10^n where n is 0, -1, -2, ... -6, and calculated a f1 score for each of the models. Our analysis shows that sample imbalance is a key challenge in training of machine learning models and can result in deceptive improvements in training cost which does not translate to true improvements in model predictive performance. Considering the wide ranging impact of the disease and its adverse effects, we developed a machine learning algorithm to treat the same. We trained our model on varying eye disease datasets consisting of over 5000 patients, and the pictures of their infected eyes. In the future, we hope this model is used extensively, especially in areas that are under-resourced, to better diagnose eye disease and improve well being for humanity.
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Imperial College London（帝国理工学院）；Medical University of Vienna（维也纳医科大学）；Moorfields Eye Hospital（摩尔菲尔德眼科医院）；Institute of Molecular and Clinical Ophthalmology Basel（巴塞尔分子与临床眼科研究所）；University of Southampton（南安普顿大学）；Technical University of Munich（慕尼黑工业大学）；University of Michigan（密歇根大学）；Universitat Basel（巴塞尔大学）
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（国家科学技术研究委员会）；Medical University of Vienna（维也纳医科大学）；VUNO Inc.（VUNO公司）；Inserm（法国国家健康与医学研究院）；Univ Bretagne Occidentale（西布列塔尼大学）；Zasti（Zasti公司）
Predicting risk of late age-related macular degeneration using deep learning
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Eye Institute (NEI), National Institutes of Health (NIH)（美国国立卫生研究院国家眼科研究所）
Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；School of Medical Science and Technology, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校医学科学与技术学院）；Department of Mathematics, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校数学系）
Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health (NIH)（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Eye Institute, National Institutes of Health（美国国立卫生研究院国家眼科研究所）；University of Wisconsin（威斯康星大学）；Erasmus Medical Center（伊拉斯谟医学中心）；Uniformed Services University of the Health Sciences（美国卫生 uniformed 大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection
Faculty of Biomedical Engineering, Technion-IIT（技术学院生物医学工程系）；Taub Faculty of Computer Science, Technion-IIT（技术学院计算机科学系）；Faculty of Mathematics, Technion-IIT（技术学院数学系）；Department of Ophthalmology, Hillel Yaffe Medical Center（希勒尔·亚法医疗中心眼科部）；The Ruth and Rappaport Faculty of Medicine, Technion-IIT（技术学院医学院）；Ophthalmology Department, São Paulo Federal University（圣保罗联邦大学眼科部）；Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学系）；The Adelson School of Medicine, Ariel University（阿里尔大学阿德勒医学学校）
Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)
BioMedIA, Imperial College London（伦敦帝国理工学院BioMedIA）；University of Southampton（南安普顿大学）；University College London（伦敦大学学院）；Moorfields Eye Hospital（摩尔菲尔德眼科医院）；Medical University of Vienna（维也纳医科大学）；Christian Doppler Forschungsgesellschaft（克里斯蒂安多普勒研究协会）；University of Basel（巴塞尔大学）；Technical University Munich（慕尼黑工业大学）
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；International Max Planck Research School for Intelligent Systems (IMPRS-IS)（国际马克斯·普朗克智能系统研究院（IMPRS-IS））；University of Stuttgart（斯图加特大学）；University of Arkansas（阿肯色大学）；Oldenburg University（奥尔登堡大学）
本文综述深度学习在糖尿病视网膜病变、青光眼等眼科疾病诊断与治疗中的前沿应用、主流架构及临床落地挑战，并展望多模态 AI 眼保健的发展方向。
Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization
University of Ibadan（伊巴丹大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Hertie Institute for AI in Brain Health（赫蒂脑健康人工智能研究所）；University of Tübingen（蒂宾根大学）；University of Bern（伯尔尼大学）；University College London（伦敦大学学院）；Tübingen AI Center（蒂宾根人工智能中心）
AI Workflow, External Validation, and Development in Eye Disease Diagnosis
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；Yale University（耶鲁大学）；National Eye Institute, National Institutes of Health（美国国立卫生研究院国家眼科研究所）；National University of Singapore（新加坡国立大学）；Sydney University（悉尼大学）；Oregon Health & Science University（俄勒冈健康与科学大学）；VA Healthcare System（退伍军人医疗系统）；Dartmouth Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；Carolina Vision Center（卡罗来纳视觉中心）；University of Miami（迈阿密大学）；Krieger Eye Institute（克里格眼科研究所）；University of California, Irvine（加利福尼亚大学尔湾分校）；Fort Belvoir Community Hospital（贝尔沃堡社区医院）；Uniformed Services University of the Health Sciences（健康科学统一服务大学）；The George Washington University（乔治华盛顿大学）；Singapore National Eye Center（新加坡国家眼科中心）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
Versatile Cataract Fundus Image Restoration Model Utilizing Unpaired Cataract and High-quality Images
Shenzhen-Platform and Carrier (International Science and Technology Information Center) & Shenzhen Bay Lab（深圳平台与载体（国际科技信息中心）及深圳湾实验室）；Capital Medical University（首都医科大学）；Beijing Tongren Hospital（北京同仁医院）
