ArticleJAMA network open2025
Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.
Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence applications facilitate decision-making in cataract surgery for highly myopic patients.Frontiers in cell and developmental biology · 2025Article
- The novel antidiabetic medications on diabetic retinopathy: relevant molecular mechanisms, advancing diagnostic innovations, and therapeutic implications.Frontiers in medicine · 2025Review
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-corrected VA without technician costs, reduce visit time, or facilitate home monitoring of VA from fundus images obtained outside of the clinic. Objective: To estimate spectacle-corrected VA measured on a standard eye chart among patients with diabetic macular edema (DME) in clinical practice settings using previously validated AI algorithms evaluating best-corrected VA from fundus photographs in eyes with DME. Design, Setting, and Participants: Retrospective cross-sectional evaluation of deidentified fundus photographs matched to spectacle-corrected VA determined by technicians on eye charts among patients with a history of DME based on optical coherence tomography and at least 2 visits within 1 to 6 months of each other at a university-based clinic between January 2014 and December 2022. Data were analyzed from January 2023 to October 2024. Exposure: Previously validated AI algorithm evaluation of fundus photographs. Main Outcomes and Measures: AI-determined VA mean absolute error (MAE) compared with actual spectacle-corrected VA. Results: Among 141 patients, the mean (SD) age was 63 (13) years, 71 (50%) were male, 2 (1%) were Asian, 42 (30%) were Black or African American, and 88 (63%) were White. Among 282 eyes at visit 1, 66 had nonproliferative diabetic retinopathy (NPDR) and DME, 38 had proliferative diabetic retinopathy (PDR) and DME, 101 had NPDR and no DME, and 77 had PDR and no DME. Among 564 images (282 eyes) at both initial and follow-up visits, MAE (SD) among eyes with NPDR, with or without center-involved DME (CI-DME), was 1.16 (1.00) lines on the eye chart for VA between 20/10 and 20/20 (67 images), and 1.44 (1.15) lines for between VA 20/25 and 20/80 (231 images). MAE (SD) among eyes with PDR, with or without CI-DME, was 1.92 (1.08) lines for VA between 20/10 and 20/20 (50 images), and 1.42 (0.97) lines for spectacle-corrected VA between 20/25 and 20/80 (150 images). Only 65 images had VA 20/100 or worse, precluding meaningful analyses. Conclusions and Relevance: In this cross-sectional study, AI evaluation of fundus photographs among patients with DME and VA 20/80 or better estimated spectacle-corrected VA within approximately 1 to 1.5 lines of actual spectacle-corrected VA. These results support use of AI evaluation of fundus photographs to determine spectacle-corrected VA among patients with DME globally, beyond ophthalmology offices.
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