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Purpose: To develop a deep convolutional neural network (DCNN) to robustly detect and grade nuclear cataract based on densitometry images obtained using deep-range anterior segment optical coherence tomography (AS-OCT).

Methods: A total of 1,309 eyes of 778 participants who underwent deep-range AS-OCT examinations were included. The tomographic images of the crystalline lenses were obtained using deep-range AS-OCT and the mean densities of the nuclei were evaluated. Lens opacities on slit-lamp images were graded according to the LOCS III system. AS-OCT images were by three ophthalmologists based on the densitometry and LOCS III scale. In total 1,309 high quality center OCT images were selected from 778 patients, in which 1074 images (320, 342, 256, and 265 images corresponding to normal, mild, moderate, and severe cases) were used to develop two DCNN models for cataract: End-to-end DCNN model and DCNN with a random forest classifier (DCNN+ RF). A total of 235 images that were collected independently were used to test the DCNN model. Gradient Class Activation Map (GradCAM) were used to visualize the outcome and to evaluate the clinical relevance.