Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning.

Journal: Ophthalmology. Retina

PMID: 38008218

Abstract

PURPOSE: To evaluate multiple machine learning (ML) models for predicting 2-year visual acuity (VA) responses to anti-vascular endothelial growth factor (anti-VEGF) treatment in the Comparison of Age-related Macular Degeneration (AMD) Treatments Trials (CATT) for patients with neovascular AMD (nAMD).

Authors

Rajat S Chandra

Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Gui-Shuang Ying

Department of Ophthalmology, Center for Preventive Ophthalmology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: gsying@pennmedicine.upenn.edu.

Keywords

Aged Aged, 80 and over Angiogenesis Inhibitors Bevacizumab Female Follow-Up Studies Humans Intravitreal Injections Machine Learning Male Ranibizumab ROC Curve Tomography, Optical Coherence Treatment Outcome Vascular Endothelial Growth Factor A Visual Acuity Wet Macular Degeneration

External Resources

View on PubMed Access via DOI PubMed (38008218)

Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning.

Abstract

Authors

Keywords

External Resources

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