Anomaly Detection and Biomarkers Localization in Retinal OCT Scans

Abstract Anomaly detection combined with localization in retinal scans can help identify retinal anomalies scans and localize pathologies that might otherwise be difficult to detect. We designed a novel approach for detecting anomalies and localization by applying AI-based tools to optical coherence tomography (OCT) scans in retinal disease. High-resolution OCT-scans from the public and a local dataset were used in four state-of-the-art self-supervised frameworks. The backbone for these frameworks was a pre-trained convolutional neural network, which allowed us to extract meaningful features from the OCT-images. Anomalous images included choroidal neovascularization, diabetic macular edema, and drusen. The resulting anomaly detectors were then evaluated using area under the receiver operating characteristic curve (ROC AUC) scores, F1 scores, and accuracy. Approximately 30000 OCT-images were used. The best-performing anomaly detectors had an ROC AUC score of 0.99, and all frameworks achieved high performance and generalized well to various retinal diseases. Using pre-trained feature extractors, the frameworks tested here can be generalized to retinal OCT-scans, yielding high image-level ROC AUC scores. The localization results obtained using these frameworks can successfully capture areas indicating the presence of retinal pathology; moreover, these frameworks may also reveal new biomarkers. Finally, these frameworks can be integrated into clinical decision-making and automated screening systems, thereby facilitating treatment management.