Classification of range of OCT-angiography capillary density using multi-channel deep-learning models in diabetic retinopathy, aging macular degeneration, and radiation retinopathy

This study developed an automated method for segmenting microvascular density regions in OCT-angiography (OCTA) images using deep learning. Four models with different input combinations were compared to determine if additional inputs improved prediction accuracy. The dataset included 50 training and 47 test images labeled by two experts. Results showed no significant differences between Expert 1 and the models, but visual inspection suggested that the model with three-channel input (OCTA + foveal avascular zone + large vessel tree) occasionally produced more consistent results. ANOVA tests compared the Dice coefficients for Expert 1, Expert 2, and the three-channel input model and found significant differences only in the normal category (p-value: 0.036), while Tukey’s HSD test showed no significant differences between each comparison. This automated approach offers a reliable alternative to manual assessments, providing consistent and objective measurements for capillary density in OCTA images.