Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes

• Published in: PeerJ (San Francisco, CA) Vol. 6; p. e5696
• Main Authors: Nagasawa, Toshihiko, Tabuchi, Hitoshi, Masumoto, Hiroki, Enno, Hiroki, Niki, Masanori, Ohsugi, Hideharu, Mitamura, Yoshinori
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 22.10.2018; PeerJ Inc
• Subjects: Algorithm; Computational Science; Convolutional neural network; Deep learning; Diagnosis; Machine learning; Macular degeneration; Macular holes; Neural networks; Ophthalmology; Ophthalmoscope and ophthalmoscopy; Optos; Wide-angle ocular fundus camera; Deep learning; Macular holes; Wide-angle ocular fundus camera; Convolutional neural network; Wide- angle camera; Optos; Algorithm
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.5696

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning.