Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials. (July 2022)

• Record Type: Journal Article
• Authors: Yaghy, Antonio; Lee, Aaron Y.; Keane, Pearse A.; Keenan, Tiarnan D.L.; Mendonca, Luisa S.M.; Lee, Cecilia S.; Cairns, Anne Marie; Carroll, Joseph; Chen, Hao; Clark, Julie; Cukras, Catherine A.; de Sisternes, Luis; Domalpally, Amitha; Durbin, Mary K.; Goetz, Kerry E.; Grassmann, Felix; Haines, Jonathan L.; Honda, Naoto; Hu, Zhihong Jewel; Mody, Christopher; Orozco, Luz D.; Owsley, Cynthia; Poor, Stephen; Reisman, Charles; Ribeiro, Ramiro; Sadda, Srinivas R.; Sivaprasad, Sobha; Staurenghi, Giovanni; Ting, Daniel SW.; Tumminia, Santa J.; Zalunardo, Luca; Waheed, Nadia K.;
• Is Part Of: Experimental eye research. Volume 220(2022)
• Publication Date: 2022-07
• Subjects: AMD Age-related macular degeneration -- Anti-VEGF Anti-vascular endothelial growth factor -- AI Artificial intelligence -- DL Deep learning -- EMR Electronic medical records -- FDA U.S. Food and Drug Administration -- GA Geographic atrophy -- ML Machine learning -- MNV Macular neovascularization -- OCT Optical coherence tomography -- OCTA Optical coherence tomography angiography; Ophthalmology -- Periodicals; Eye -- Periodicals; Œil -- Périodiques; Ophthalmology; Periodicals; Electronic journals; 612.8405
• DOI: 10.1016/j.exer.2022.109092
• Languages: English
• ISSNs: 0014-4835

Abstract

Highlights: Machine learning models predict risk for adverse events and likelihood of dropout. Machine learning models predict disease progression risk and response to treatment. Predictive models can be of great use in all stages of clinical trials. Training data sets have to be diverse to maximize generalizability. It is key to ensure patient safety and ethical use of the models.