Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images

Abstract

Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model’s performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.

Operating Artificial Intelligence to Assist Physicians Diagnose Medical Images: A Narrative Review

“…• Corneal Disorders [30]: AI algorithms can examine data from topography or tomography scans of the cornea to help in the identification of conditions like keratoconus, corneal dystrophies, or corneal infections. • Refractive Error Assessments [31]: Using data from autorefractors and retinoscopes as well as other clinical data, AI algorithms can analyse refractive faults including myopia, hyperopia, and astigmatism to produce more precise readings.…”