Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis.

Background: Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke. Study design: A case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results. Results: 711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983). Conclusion: A fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.

Estimation of stroke severity with National Institutes of Health Stroke Scale grading and retinal features: A cross-sectional study.

ABSTRACT: To estimate National Institutes of Health Stroke Scale (NIHSS) grading of stroke patients with retinal characteristics.A cross-sectional study was conducted in Shenzhen Traditional Chinese Medicine Hospital. Baseline information and retinal photos were collected within 2 weeks of admission. An NIHSS score was measured for each patient by trained doctors. Patients were classified into 0 to 4 score group and 5 to 42 score group for analysis. Three multivariate logistic models, with traditional clinical characteristics alone, with retinal characteristics alone, and with both, were built.For clinical characteristics, hypertension duration is statistically significantly associated with higher NIHSS score (P = .014). Elevated total homocysteine levels had an OR of 0.456 (P = .029). For retinal characteristics, the fractal dimension of the arteriolar network had an OR of 0.245 (P < .001) for the left eyes, and an OR of 0.417 (P = .009) for right eyes. The bifurcation coefficient of the arteriole of the left eyes had an OR of 2.931 (95% CI 1.573-5.46, P = .001), the nipping of the right eyes had an OR of 0.092 (P = .003) showed statistical significance in the model.The area under receiver-operating characteristic curve increased from 0.673, based on the model with clinical characteristics alone, to 0.896 for the model with retinal characteristics alone and increased to 0.931 for the model with both clinical and retinal characteristics combined.Retinal characteristics provided more information than clinical characteristics in estimating NIHSS grading and can provide us with an objective method for stroke severity estimation.

Efficacy of verum and sham acupoint catgut embedding for treatment of obesity: Study protocol for a randomized controlled trial.

BACKGROUND: Obesity has become a major public health hazard with epidemic proportions, affecting adults, adolescents, and children of both genders. Previous studies have suggested that acupoint catgut embedding (ACE) might be a potential therapeutic approach for obesity. The purpose of this study is to conduct a rigorous and normative trial to determine the efficacy of ACE for obesity. METHODS/DESIGN: A total of 99 eligible patients diagnosed with obesity will be recruited in this study. They will be randomly allocated to either the verum ACE group, sham ACE group, or waiting list (WL) group, with 33 patients in each group. Each patient in the two ACE-based groups will receive eight sessions of treatment, lasting over 8 weeks. The primary outcome is the reduction of body mass index (BMI) after treatment. Secondary outcomes will include waist circumference (WC), hip circumference (HC), waist:hip ratio, body fat percentage, blood lipid level, subcutaneous fat area, visceral fat area, and World Health Organization Quality of Life (WHOQOL). All the outcomes will be evaluated at baseline, at the end of the 8 weeks of treatments, and at 3 months of follow-up. The evaluators and data analyzers will be blinded to group allocation. DISCUSSION: The findings of this randomized, sham-, and WL-controlled trial will help to investigate the influence of ACE on clinical variables as well as visceral fat area of obesity, which will provide high-quality evidence on the efficacy of ACE for obesity. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR1800020248. Registered on December 21, 2018.