Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images

Purpose: This study was designed to apply deep learning models in retinal disease screening and lesion detection based on optical coherence tomography (OCT) images.Methods: We collected 37,138 OCT images from 775 patients and labelled by ophthal-mologists. Multiple deep learning models including ResNet50 and YOLOv3 were devel-oped to identify the types and locations of diseases or lesions based on the images.Results: The model were evaluated using patient-based independent holdout set. For binary classification of OCT images with or without lesions, the performance accuracy was 98.5%, sensitivity was 98.7%, specificity was 98.4%, and the F1 score was 97.7%. For multiclass multilabel disease classification, the models was able to detect vitreomac-ular traction syndrome and age-related macular degeneration both with an accuracy of more than 99%, sensitivity of more than 98%, specificity of more than 98%, and an F1 score of more than 97%. For lesion location detection, the recalls for different lesion types ranged from 87.0% (epiretinal membrane) to 98.2% (macular pucker).Conclusions: Deep learning-based models have potentials to aid retinal disease screen-ing, classification and diagnosis with excellent performance, which may serve as useful references for ophthalmologists.Translational Relevance: The deep learning-based models are capable of identify-ing and predicting different eye diseases and lesions from OCT images and may have potential clinical application to assist the ophthalmologists for fast and accuracy retinal disease screening.