Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images

• Main Authors: Hiroki Masumoto, Hitoshi Tabuchi, Shunsuke Nakakura, Hideharu Ohsugi, Hiroki Enno, Naofumi Ishitobi, Eiko Ohsugi, Yoshinori Mitamura
• Format: Article
• Language: English
• Published: PeerJ Inc. 2019-05-01
• Series: PeerJ
• Subjects: Neural network; Retinitis pigmentosa; Screening system; Ultrawide-filed pseudocolor imaging; Ultrawide-field autofluorescence
• Online Access: https://peerj.com/articles/6900.pdf

Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted. We examined the K-fold cross validation (K = 5). The mean area under the curve of the ultrawide-field pseudocolor group was 0.998 (95% confidence interval (CI) [0.9953–1.0]) and that of the ultrawide-field autofluorescence group was 1.0 (95% CI [0.9994–1.0]). The sensitivity and specificity of the ultrawide-field pseudocolor group were 99.3% (95% CI [96.3%–100.0%]) and 99.1% (95% CI [96.1%–99.7%]), and those of the ultrawide-field autofluorescence group were 100% (95% CI [97.6%–100%]) and 99.5% (95% CI [96.8%–99.9%]), respectively. Heatmaps were in accordance with the clinician’s observations. Using the proposed deep neural network model, retinitis pigmentosa can be distinguished from healthy eyes with high sensitivity and specificity on ultrawide-field pseudocolor and ultrawide-field autofluorescence images.