Qu Yimin, Zhuo Yuanyuan, Lee Jack, Huang Xingxian, Yang Zhuoxin, Yu Haibo, Zhang Jinwen, Yuan Weiqu, Wu Jiaman, Owens David, Zee Benny
Centre for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
Centre for Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research Institute, Shenzhen, China.
Front Neurol. 2022 Aug 22;13:916966. doi: 10.3389/fneur.2022.916966. eCollection 2022.
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.
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.
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).
A fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
中风是全球第二大致死原因，造成了相当大的疾病负担。缺血性中风更为常见，但出血性中风导致的死亡更多。临床管理和治疗方法不同，尽早对其风险进行分类以预防疾病是有益的。此外，视网膜特征与中风有关，可用于中风风险评估。本研究调查了用于视网膜图像的机器学习方法，以评估缺血性和出血性中风的风险并进行分类。
在深圳市中医院进行了一项病例对照研究。根据计算机断层扫描（CT）或磁共振成像（MRI）结果，中风患者被分为缺血性或出血性中风。此外，使用来自医院的非中风患者和社区的健康个体组成对照组。从参与者的医院病历中收集基线人口统计学和医学信息。在入院后2周内拍摄每个参与者双眼的视网膜图像。开发了使用机器学习方法的分类模型。采用10折交叉验证方法验证结果。
纳入711例患者，其中缺血性中风患者145例，出血性中风患者86例，对照组480例。基于10折交叉验证，缺血性中风风险评估的敏感性和特异性分别为91.0%和94.8%。缺血性中风的ROC曲线下面积为0.929（95%CI 0.900至0.958）。出血性中风风险评估的敏感性和特异性分别为93.0%和97.1%。ROC曲线下面积为0.951（95%CI 0.918至0.983）。
一种快速且全自动的方法可用于仅基于眼底照片的中风亚型风险评估和分类。
