SimulaMet；Oslo Metropolitan University（奥斯陆城市大学）；Odense University Hospital（欧登塞大学医院）；Ifocus；Oslo University Hospital（奥斯陆大学医院）
Andrea M. Storås, Inga Strümke, Michael A. Riegler, Jakob Grauslund, Hugo L. Hammer, Anis Yazidi, Pål Halvorsen, Kjell G. Gundersen, Tor P. Utheim, Catherine Jackson
Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term `AI' is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.
SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Beihang University（北京航空航天大学）；Beijing Tongren Hospital, Capital Medical University（首都医科大学附属北京同仁医院）；The University of Melbourne（墨尔本大学）；University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）
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）
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）
Detecting Alzheimer's Disease by estimating attention and elicitation path through the alignment of spoken picture descriptions with the picture prompt
University of Sheffield（谢菲尔德大学）；Royal Hallamshire Hospital（皇家哈勒姆医院）；Sheffield Institute for Translational Neuroscience（谢菲尔德转化神经科学研究所）
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks
