Abstract：
Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is not yet sufficient for monitoring glaucoma progression in isolation. There is an urgent need to develop more comprehensive diagnostic methods with higher accuracy. In this study, we proposed a multi- feature deep learning (MFDL) system based on intraocular pressure (IOP), color fundus photograph (CFP) and visual field (VF) to classify the glaucoma into four severity levels. We designed a three-phase framework for glaucoma severity diagnosis from coarse to fine, which contains screening, detection and classification. We trained it on 6,131 samples from 3,324 patients and tested it on independent 240 samples from 185 patients. Our results show that MFDL achieved a higher accuracy of 0.842 (95 % CI, 0.795-0.888) than the direct four classification deep learning (DFC-DL, accuracy of 0.513 [0.449-0.576]), CFP-based single-feature deep learning (CFP-DL, accuracy of 0.483 [0.420-0.547]) and VF-based single-feature deep learning (VF-DL, accuracy of 0.725 [0.668-0.782]). Its performance was statistically significantly superior to that of 8 juniors. It also outperformed 3 seniors and 1 expert, and was comparable with 2 glaucoma experts (0.842 vs 0.854, p = 0.663; 0.842 vs 0.858, p = 0.580). With the assistance of MFDL, junior ophthalmologists achieved statistically significantly higher accuracy performance, with the increased accuracy ranged from 7.50 % to 17.9 %, and that of seniors and experts were 6.30 % to 7.50 % and 5.40 % to 7.50 %. The mean diagnosis time per patient of MFDL was 5.96 s. The proposed model can potentially assist ophthalmologists in efficient and accurate glaucoma diagnosis that could aid the clinical management of glaucoma.
摘要：
青光眼是导致不可逆性失明的主要原因,早期发现和及时治疗对青光眼管理至关重要。然而,由于青光眼发病特征的个体差异，一个单一的特征还不足以单独监测青光眼的进展.迫切需要开发具有更高准确性的更全面的诊断方法。在这项研究中,我们提出了一种基于眼压（IOP）的多特征深度学习（MFDL）系统，彩色眼底照片（CFP）和视野（VF）将青光眼分为四个严重程度。我们设计了一个从粗到细的青光眼严重程度诊断的三阶段框架，其中包含筛选，检测和分类。我们对来自3,324名患者的6,131个样本进行了训练，并对来自185名患者的240个独立样本进行了测试。我们的结果表明，MFDL比直接四分类深度学习（DFC-DL，精度为0.513[0.449-0.576])，基于CFP的单特征深度学习(CFP-DL、0.483[0.420-0.547]的精度)和基于VF的单特征深度学习(VF-DL，精度为0.725[0.668-0.782])。其表现在统计学上显着优于8名大三学生。它还胜过3名老年人和1名专家，与2位青光眼专家相当（0.842vs0.854，p=0.663；0.842vs0.858，p=0.580）。在MFDL的协助下,初级眼科医生取得了统计学上显著更高的准确性表现，增加的准确度范围从7.50%到17.9%，老年人和专家的比例分别为6.30%至7.50%和5.40%至7.50%。每个MFDL患者的平均诊断时间为5.96s。所提出的模型可以潜在地帮助眼科医生进行有效和准确的青光眼诊断，从而有助于青光眼的临床管理。
