Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images

Background/Objectives: This study aimed to evaluate a deep learning model for estimating uncorrected refractive error using posterior segment optical coherence tomography (OCT) images. Methods: In this retrospective study, we assigned healthy subjects to development (N= 688 eyes of 344 subjects) and test (N= 248 eyes of 124 subjects) datasets (prospective validation design). We developed and validated OCT-based deep learning models to estimate refractive error. A regression model based on a pretrained ResNet50 architecture was trained using horizontal OCT images to predict the spherical equivalent (SE). The performance of the deep learning model for detecting high myopia was also evaluated. A saliency map was generated using the Grad-CAM technique to visualize the characteristic features. Results: The developed model showed a low mean absolute error for SE prediction (2.66 D) and a significant Pearson correlation coefficient of 0.588 (P< 0.001) in the test dataset validation. To detect high myopia, the model yielded an area under the receiver operating characteristic curve of 0.813 (95% confidence interval [CI], 0.744–0.881) and an accuracy of 71.4% (95% CI, 65.3–76.9%). The inner retinal layers and relatively steepened curvatures were highlighted using a saliency map to detect high myopia. Conclusion: A deep learning algorithm showed that OCT could potentially be used as an imaging modality to estimate refractive error. This method will facilitate the evaluation of refractive error to prevent clinicians from overlooking the risks associated with refractive error during OCT assessment.