Imaging and artificial intelligence for progression of age-related macular degeneration |
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Authors: | Kathleen Romond Minhaj Alam Sasha Kravets Luis de Sisternes Theodore Leng Jennifer I Lim Daniel Rubin Joelle A Hallak |
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Affiliation: | 1.Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA; 2.Department of Biomedical Data Science, Stanford University, Stanford, CA 94304, USA; 3.Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60612, USA; 4.Carl Zeiss Meditec, Inc., Dublin, CA 94568, USA; 5.Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, CA 94303, USA |
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Abstract: | Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications. |
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Keywords: | Artificial intelligence machine learning deep learning age-related macular degeneration disease progression imaging modalities |
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