Zhuo Y Y, Wu J M, Kuang L, Qu Y M, Zee B, Lee J, Yang Z X
Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen 518033, China.
Department of Chinese Medicine, Affiliated Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen 518028, China.
Evid Based Complement Alternat Med. 2020 Mar 26;2020:6051831. doi: 10.1155/2020/6051831. eCollection 2020.
We aimed to investigate the efficacy of an objective method using AI-based retinal characteristic analysis to automatically differentiate between two traditional Chinese syndromes that are associated with ischemic stroke. Inpatient clinical and retinal data were retrospectively retrieved from the archive of our hospital. Patients diagnosed with cerebral infarction in the department of acupuncture and moxibustion between 2014 and 2018 were examined. Of these, the patients with Qi deficiency blood stasis syndrome (QDBS) and phlegm stasis in channels (PSIC) syndrome were selected. Those without retinal photos were excluded. To measure and analyze the patients' retinal vessel characteristics, we applied a patented AI-assisted automated retinal image analysis system developed by the Chinese University of Hong Kong. The demographic, clinical, and retinal information was compared between the QDBS and PSIC patients. The -test and chi-squared test were used to analyze continuous data and categorical data, respectively. All the selected clinical information and retinal vessel measures were used to develop different discriminative models for QDBS and PSIC using logistic regression. Discriminative efficacy and model performances were evaluated by plotting a receiver operating characteristic curve. As compared to QDBS, the PSIC patients had a lower incidence of insomnia problems (46% versus 29% respectively, =0.023) and a higher tortuosity index (0.45 ± 0.07 versus 0.47 ± 0.07, =0.027). Moreover, the area under the curve of the logistic model showed that its discriminative efficacy based on both retinal and clinical characteristics was 86.7%, which was better than the model that employed retinal or clinical characteristics individually. Thus, the discriminative model using AI-assisted retinal characteristic analysis showed statistically significantly better performance in QDBS and PSIC syndrome differentiation among stroke patients. Therefore, we concluded that retinal characteristics added value to the clinical differentiation between QDBS and PSIC.
我们旨在研究一种基于人工智能的视网膜特征分析的客观方法在自动区分两种与缺血性中风相关的中医证候方面的疗效。从我院存档中回顾性检索住院患者的临床和视网膜数据。对2014年至2018年期间在针灸科被诊断为脑梗死的患者进行检查。其中，选取了气虚血瘀证（QDBS）和痰瘀阻络证（PSIC）的患者。排除没有视网膜照片的患者。为了测量和分析患者的视网膜血管特征，我们应用了香港中文大学开发的专利人工智能辅助自动视网膜图像分析系统。比较了QDBS和PSIC患者的人口统计学、临床和视网膜信息。分别使用t检验和卡方检验分析连续数据和分类数据。所有选定的临床信息和视网膜血管测量数据用于使用逻辑回归为QDBS和PSIC建立不同的判别模型。通过绘制受试者工作特征曲线来评估判别效能和模型性能。与QDBS相比，PSIC患者失眠问题的发生率较低（分别为46%和29%，P = 0.023），迂曲指数较高（0.45±0.07对0.47±0.07，P = 0.027）。此外，逻辑模型的曲线下面积表明，其基于视网膜和临床特征的判别效能为86.7%，优于单独使用视网膜或临床特征的模型。因此，使用人工智能辅助视网膜特征分析的判别模型在中风患者的QDBS和PSIC证候鉴别中表现出统计学上显著更好的性能。因此，我们得出结论，视网膜特征为QDBS和PSIC的临床鉴别增加了价值。
