Abstract
Retinitis pigmentosa (RP) is one group of inherited retinal diseases that are caused by genetic defects that lead to progressive photoreceptor loss and eventual blindness. Early diagnosis will helpful for a effective management of the disease, however, many patients remain unaware of eraly symptoms. Meanwhile, fundus images are widely taken for medical checkups, however, are underused in detecting RP. This study explores the potential of deep learning to identify RP from color fundus images. The dataset contained 200 color fundus images of Japanese RP patients and 121 color fundus images from non-RP subjects from Keio University Hospital. Using transfer learning, pretrained convolutional neural network models -VGG16, Resnet50, and InceptionV3- were finetuned to detect RP. As a result, Inception V3 achieved the best accuracy of 96.97%, which matches the average diagnostic accuracy of ophthalmologists. Using Gradient-weighted Class Activation Mapping (Grad-CAM), we identified peripheral pigmentation in the fundus images as a critical feature for diagnosis, aligning with the known progression patterns of RP. This confirms the robustness and validity of our model, highlighting the utility of deep learning in assisting ophthalmologists with RP screening.