Classification of Optic Disc Pathologies in Near Infrared Reflectance Images with Deep Learning

Background: This study aimed to develop an artificial intelligence-based deep learning (DL) algorithm using near-infrared reflectance (NIR) images to differentiate between optic disc edema and pseudopapilledema, and to evaluate the diagnostic performance of the developed model. Methods: NIR images were divided into 2 groups for training and testing of the model. 85% (714 images) were used for training the model and 15% (126 images) for testing the trained model. Sensitivity, specificity and accuracy of the model were calculated in detecting optic disc edema, pseudopapilledema and normal optic discs. Receiver operating characteristic curve and area under the curve (AUC) values were also analyzed. Results: The developed model was tested with 24 optic disc edema, 52 pseudopapilledema, and 50 normal optic disc images not used in training. Sensitivities were 100%, 98%, and 96%; specificities were 99%, 97%, and 100%; and accuracy rates were 99%, 98%, and 98%, respectively. In addition, the AUC values of the groups were 0.995 (95% Confidence interval [CI]: 0.98-1); 0.983 (95% CI: 0.96-1); and 0.973 (95% CI: 0.94-1). Conclusions: The developed DL model demonstrated high diagnostic performance in distinguishing optic disc edema and pseudopapilledema using NIR images and may serve as a reliable clinical decision support tool.