The Discriminative Efficacy of Retinal Characteristics on Two Traditional Chinese Syndromes in Association with Ischemic Stroke. (26th March 2020)

• Record Type: Journal Article
• Authors: Zhuo, Y. Y.; Wu, J. M.; Kuang, L.; Qu, Y. M.; Zee, B.; Lee, J.; Yang, Z. X.
• Other Names: Andrade-Cetto Adolfo Academic Editor.
• Is Part Of: Evidence-based complementary and alternative medicine. Volume 2020(2020)
• Publication Date: 2020-03-26
• Subjects: Alternative medicine -- Periodicals; 615.505
• DOI: 10.1155/2020/6051831
• Languages: English
• ISSNs: 1741-427X

Abstract

Abstract : 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 t -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, p = 0.023 ) and a higher tortuosity index (0.45 ± 0.07 versus 0.47 ± 0.07, p = 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.