Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion

作     者：Daisuke Nagasato Hitoshi Tabuchi Hideharu Ohsugi Hiroki Masumoto Hiroki Enno Naofumi Ishitobi Tomoaki Sonobe Masahiro Kameoka Masanori Niki Yoshinori Mitamura 

作者机构：Department of OphthalmologySaneikai Tsukazaki Hospital Rist Inc Department of OphthalmologyInstitute of Biomedical SciencesTokushima University Graduate School 

出 版 物：《International Journal of Ophthalmology(English edition)》 (国际眼科杂志（英文版）)

年 卷 期：2019年第12卷第1期

页      面：94-99页

核心收录：

学科分类：10[医学] 

主　　题：automatic diagnosis branch retinal vein occlusion deep learning machine-learning technology ultrawide-field fundus ophthalmoscopy 

摘      要：AIM: To investigate and compare the efficacy of two machine-learning technologies with deep-learning(DL) and support vector machine(SVM) for the detection of branch retinal vein occlusion(BRVO) using ultrawide-field fundus images. METHODS: This study included 237 images from 236 patients with BRVO with a mean±standard deviation of age 66.3±10.6 y and 229 images from 176 non-BRVO healthy subjects with a mean age of 64.9±9.4 y. Training was conducted using a deep convolutional neural network using ultrawide-field fundus images to construct the DL model. The sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV) and area under the curve(AUC) were calculated to compare the diagnostic abilities of the DL and SVM models. RESULTS: For the DL model, the sensitivity, specificity, PPV, NPV and AUC for diagnosing BRVO was 94.0%(95%CI: 93.8%-98.8%), 97.0%(95%CI: 89.7%-96.4%), 96.5%(95%CI: 94.3%-98.7%), 93.2%(95%CI: 90.5%-96.0%) and 0.976(95%CI: 0.960-0.993), respectively. In contrast, for the SVM model, these values were 80.5%(95%CI: 77.8%-87.9%), 84.3%(95%CI: 75.8%-86.1%), 83.5%(95%CI: 78.4%-88.6%), 75.2%(95%CI: 72.1%-78.3%) and 0.857(95%CI: 0.811-0.903), respectively. The DL model outperformed the SVM model in all the aforementioned parameters(P0.001). CONCLUSION: These results indicate that the combination of the DL model and ultrawide-field fundus ophthalmoscopy may distinguish between healthy and BRVO eyes with a high level of accuracy. The proposed combination may be used for automatically diagnosing BRVO in patients residing in remote areas lacking access to an ophthalmic medical center.