Journal Article

Fundus image analysis of retinitis pigmentosa using artificial intelligence

Yang, Lizhu, Masayoshi, Kanato, Ozawa, Nobuhiro, Ubukata, Saki, Negishi, Kazuno, Ibuki, Mari, Kurihara, Toshihide, Katada, Yusaku

2026

Overview

Retinitis pigmentosa (RP) is a group of inherited retinal diseases that are caused by genetic defects that lead to progressive photoreceptor loss and eventual blindness. Early diagnosis would be helpful for effective management of the disease; however, many patients stay unaware of early symptoms. Meanwhile, fundus images are widely obtained during routine medical checkups but are underused for detecting RP. This study explores the effectiveness of finetuning deep learning models, pre-trained for general visual tasks, to identify RP from color fundus images. The dataset comprised 321 color fundus images from 201 Japanese subjects at Keio University Hospital, including 200 images from 107 patients with retinitis pigmentosa and 121 images from 94 non-retinitis pigmentosa subjects. Multiple images were available for some subjects. 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. Gradient-weighted Class Activation Mapping (Grad-CAM) suggested that the model attended to clinically relevant fundus regions, including the peripheral retina and posterior pole, which may reflect features such as peripheral degenerative changes and retinal vascular attenuation. These findings support the potential interpretability of the finetuned model and suggest that deep learning may assist ophthalmologists in RP screening as a supportive tool.

• Publisher: Public Library of Science,PLOS
• Subject: Accuracy / Artificial Intelligence / Artificial neural networks / Biology and Life Sciences / Classification / Clinical decision making / Clinical medicine / Color / Color vision / Computer and Information Sciences / Convolutional Neural Networks / Data integrity / Datasets / Deep Learning / Disease / Effectiveness / Family medical history / Female / Field study / Fundus Oculi / Genes / Glaucoma / Humans / Image analysis / Image processing / Image Processing, Computer-Assisted - methods / Machine learning / Medical imaging / Medical personnel / Medical records / Medicine and Health Sciences / Neural networks / Patients / Performance evaluation / R&D / Research & development / Retina / Retinitis pigmentosa / Retinitis Pigmentosa - diagnosis / Retinitis Pigmentosa - diagnostic imaging / Retinitis Pigmentosa - pathology / Social Sciences / Transfer learning / Visual impairment / Visual tasks