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

PeerJ

article

Toshihiko Nagasawa¹  Hitoshi Tabuchi¹  Hiroki Masumoto¹  Hiroki Enno²  Masanori Niki³  Hideharu Ohsugi¹  Yoshinori Mitamura³ 

[1] Department of Ophthalmology, Tsukazaki Hospital;Rist Inc.;Department of Ophthalmology, Institute of Biomedical Sciences, Tokushima University

关键词: Wide-angle ocular fundus camera;    Macular holes;    Deep learning;    Optos;    Convolutional neural network;    Algorithm;    Wide- angle camera;   

DOI  :  10.7717/peerj.5696

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY