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 ophthalmologists. Multiple deep learning models including ResNet50 and YOLOv3 were developed 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 vitreomacular 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 screening, classification and diagnosis with excellent performance, which may serve as useful references for ophthalmologists. TRANSLATIONAL RELEVANCE: The deep learning-based models are capable of identifying 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.