- Avinash Vaidyanathan Varadarajan
- Ryan Poplin
- Katy Blumer
- Christof Angermueller
- Joe Ledsam
- Reena Chopra
- Pearse Keane
- Greg Corrado
- Lily Peng
- Dale Webster
Objective: Refractive error, one of the leading cause of visual impairment, can be corrected by simple interventions like prescribing eyeglasses, which often starts with autorefraction to estimate the refractive error. In this study, using deep learning, we trained a network to estimate refractive error from fundus photos only.
Design: Retrospective analysis.
Subjects, Participants, and/or Controls: Retinal fundus images from participants in the UK Biobank cohort, which were 45 degree field of view images and the AREDS clinical trial, which contained 30 degree field of view images.
Methods, Intervention, or Testing: Refractive error was measured by autorefraction in the UK Biobank dataset and subjective refraction in the AREDS dataset. We trained a deep learning algorithm to predict refractive error from the fundus photographs and tested the prediction of the algorithm to the documented refractive error measurement. Our model used attention for identifying features that are predictive for refractive error.
Main Outcome Measures: Mean average error (MAE) of the algorithm’s prediction compared to the refractive error obtained in the AREDS and UK Biobank.
Results: The resulting algorithm had a mean average error (MAE) of 0.56 diopters (95% CI: 0.55-0.56) for estimating spherical equivalent on the UK Biobank dataset and 0.91 diopters (95% CI: 0.89-0.92) for the AREDS dataset. The baseline expected MAE (obtained by simply predicting the mean of this population) is 1.81 diopters (95% CI: 1.79-1.84) for UK Biobank and 1.63 (95% CI: 1.60-1.67) for AREDS. Attention maps suggest that the foveal region is one of the most important areas that is used by the algorithm to make this prediction, though other regions also contribute to the prediction.
Conclusions: The ability to estimate refractive error with high accuracy from retinal fundus photos has not been previously known and demonstrates that deep learning can be applied to make novel predictions from medical images. In addition, given that several groups have recently shown that it is feasible to obtain retinal fundus photos using mobile phones and inexpensive attachments, this work may be particularly relevant in regions of the world where autorefractors may not be readily available.