{"doi":"10.1016/j.xops.2022.100160","title":"Machine Learning OCT Predictors of Progression from Intermediate Age-Related Macular Degeneration to Geographic Atrophy and Vision Loss","abstract":"Objective: To describe optical coherence tomography (SD-OCT) features, age, gender, and systemic variables that may be used in machine/deep learning studies to identify high-risk patient subpopulations with high risk of progression to geographic atrophy (GA) and visual acuity (VA) loss in the short term. Design: prospective, longitudinal study. Subjects: We analyzed imaging data from patients with iAMD (N= 316) enrolled in Age-Related Eye Disease Study 2 (AREDS2) Ancillary SD-OCT with adequate SD-OCT imaging for repeated measures. Methods: Qualitative and quantitative multimodal variables from the database were derived at each yearly visit over 5 years. Based on statistical analyses developed in the field of cardiology, an algorithm was developed and used to select person-years without GA on colour fundus photography or SD-OCT at baseline. The analysis employed machine learning approaches to generate classification trees. Eyes were stratified as low, average, above average and high risk in 1 or 2 years, based on OCT and demographic features by the risk of GA development or decreased VA by 5+ and 10+ letters. Main outcome measures: new onset of SD-OCT-determined GA and VA loss. Results: We identified multiple retinal and subretinal SD-OCT and demographic features from the baseline visit, each of which independently conveyed low to high risk of new-onset GA or VA loss on each of the follow-up visits at 1 or 2 years. Conclusion: We propose a risk-stratified classification of iAMD based on the combination of OCT-derived retinal features, age, gender and systemic variables for progression to OCT-determined GA and/or VA loss. After external validation, the composite early endpoints may be used as exclusion or inclusion criteria for future clinical studies of iAMD focused on prevention of GA progression or VA loss.","journal":"Ophthalmology Science","year":2022,"id":255261,"datarank":0.4566783656585135,"base_score":3.044522437723423,"endowment":3.044522437723423,"self_citation_contribution":0.4566783656585135,"citation_network_contribution":0.0,"self_endowment_contribution":0.4566783656585135,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":20,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6943,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":509570,"name":"Karim Sleiman","orcid":"0000-0002-4017-4908","position":1,"is_corresponding":false},{"id":903003,"name":"David Banks","orcid":"0000-0002-2376-0710","position":2,"is_corresponding":false},{"id":903537,"name":"Sanjay Hariharan","orcid":null,"position":3,"is_corresponding":false},{"id":254975,"name":"Traci E. 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