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Paul W, Burlina P, Mocharla R, Joshi N, Li Z, Gu S, Nanegrungsunk O, Lin K, Bressler SB, Cai CX, Kong J, Liu TYA, Moini H, Du W, Amer F, Chu K, Vitti R, Sepehrband F, Bressler NM. Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema. JAMA Ophthalmol 2023; 141:677-685. [PMID: 37289463 PMCID: PMC10251243 DOI: 10.1001/jamaophthalmol.2023.2271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/17/2023] [Indexed: 06/09/2023]
Abstract
Importance Best-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti-vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely. Objective To evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information. Design, Setting, and Participants Deidentified color fundus images taken after dilation were used post hoc to train AI systems to perform regression from image to BCVA and to evaluate resultant estimation errors. Participants were patients enrolled in the VISTA randomized clinical trial through 148 weeks wherein the study eye was treated with aflibercept or laser. The data from study participants included macular images, clinical information, and BCVA scores by trained examiners following protocol refraction and VA measurement on Early Treatment Diabetic Retinopathy Study (ETDRS) charts. Main Outcomes Primary outcome was regression evaluated by mean absolute error (MAE); the secondary outcome included percentage of predictions within 10 letters, computed over the entire cohort as well as over subsets categorized by baseline BCVA, determined from baseline through the 148-week visit. Results Analysis included 7185 macular color fundus images of the study and fellow eyes from 459 participants. Overall, the mean (SD) age was 62.2 (9.8) years, and 250 (54.5%) were male. The baseline BCVA score for the study eyes ranged from 73 to 24 letters (approximate Snellen equivalent 20/40 to 20/320). Using ResNet50 architecture, the MAE for the testing set (n = 641 images) was 9.66 (95% CI, 9.05-10.28); 33% of the values (95% CI, 30%-37%) were within 0 to 5 letters and 28% (95% CI, 25%-32%) within 6 to 10 letters. For BCVA of 100 letters or less but more than 80 letters (20/10 to 20/25, n = 161) and 80 letters or less but more than 55 letters (20/32 to 20/80, n = 309), the MAE was 8.84 letters (95% CI, 7.88-9.81) and 7.91 letters (95% CI, 7.28-8.53), respectively. Conclusions and Relevance This investigation suggests AI can estimate BCVA directly from fundus photographs in patients with DME, without refraction or subjective visual acuity measurements, often within 1 to 2 lines on an ETDRS chart, supporting this AI concept if additional improvements in estimates can be achieved.
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Affiliation(s)
- William Paul
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
| | - Philippe Burlina
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
- Department of Computer Science and Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
- Zoox, Foster City, California
| | - Rohita Mocharla
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
| | - Neil Joshi
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
| | - Zhuolin Li
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sophie Gu
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York–Presbyterian Hospital, New York, New York
| | - Onnisa Nanegrungsunk
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Kira Lin
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Ruiz Department of Ophthalmology and Visual Science at McGovern Medical School at UTHealth Houston, Houston, Texas
| | - Susan B. Bressler
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Cindy X. Cai
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jun Kong
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - T. Y. Alvin Liu
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hadi Moini
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Weiming Du
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Fouad Amer
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Karen Chu
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Robert Vitti
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | | | - Neil M. Bressler
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Editor, JAMA Ophthalmology
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