An Intelligent Optical Coherence Tomography-based System for Pathological Retinal Cases Identification and Urgent Referrals

PURPOSE: This study aimed to develop an automated system with artificial intelligence algorithms to comprehensively identify pathologic retinal cases and make urgent referrals. METHODS: To build and test the intelligent system, this study obtained 28,664 optical coherence tomography (OCT) images from 2254 patients in the Eye and ENT Hospital of Fudan University (EENT Hospital) and Shanghai Tenth People's Hospital (TENTH Hospital). We applied a deep learning model with an adapted feature pyramid network to detect 15 categories of retinal pathologies from OCT images as common signs of various retinal diseases. Subsequently, the pathologies detected in the OCT images and thickness features extracted from retinal thickness measurements were combined for urgent referral using the random forest tool. RESULTS: The retinal pathologies detection model had a sensitivity of 96.39% and specificity of 98.91% from the EENT Hospital test dataset, whereas those from the TENTH Hospital test dataset were 94.89% and 98.76%, respectively. The urgent referral model achieved accuracies of 98.12% and 98.01% from the EENT Hospital and TENTH Hospital test datasets, respectively. CONCLUSIONS: An intelligent system capable of automatically identifying pathologic retinal cases and offering urgent referrals was developed and demonstrated reliable performance with high sensitivity, specificity, and accuracy. TRANSLATIONAL RELEVANCE: This intelligent system has great value and practicability in communities where exist increasing cases of retinal disease and a lack of ophthalmologists.