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

Yimin Qu; Yuanyuan Zhuo; Jack Lee; Xingxian Huang; Zhuoxin Yang; Haibo Yu; Jinwen Zhang; Weiqu Yuan; Jiaman Wu; David Owens; Benny Zee

doi:10.3389/fneur.2022.916966

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

Front Neurol. 2022 Aug 22:13:916966. doi: 10.3389/fneur.2022.916966. eCollection 2022.

Authors

Yimin Qu^{1

2

3}, Yuanyuan Zhuo⁴, Jack Lee^{1

2}, Xingxian Huang⁴, Zhuoxin Yang⁴, Haibo Yu⁴, Jinwen Zhang⁴, Weiqu Yuan⁴, Jiaman Wu⁵, David Owens⁶, Benny Zee^{1

2}

Affiliations

¹ Centre for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.
² Centre for Clinical Trials and Biostatistics Lab, CUHK Shenzhen Research Institute, Shenzhen, China.
³ School of Population Medicine and Public Health, Chinese Academy of Medical Science (CAMS) and Peking Union Medical College (PUMC), Beijing, China.
⁴ Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China.
⁵ Affiliated Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
⁶ Swansea University, Wales, United Kingdom.

Abstract

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.

Keywords: haemorrhagic stroke; ischemic stroke; machine-learning method; retinal image analysis; stroke subtypes classification.