Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning

Frontiers in Cardiovascular Medicine

Cardiovascular Medicine

Haitao Zhang¹  Qiushi Ren²  Hui Li²  Mengdi Gao²  Yiwei Cui³  Gang Li³  Yang Li³  Haiqing Song⁴  Xiao Wu⁴  Zhaoheng Xie⁵ 

[1] Cardio-Metabolic Medicine Center, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China;Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China;Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China;Shenzhen Bay Laboratory, Institute of Biomedical Engineering, Shenzhen, China;National Biomedical Imaging Center, Peking University, Beijing, China;Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China;Department of Cardiology, Beijing Yanhua Hospital, Beijing, China;Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China;Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China;Shenzhen Bay Laboratory, Institute of Biomedical Engineering, Shenzhen, China;National Biomedical Imaging Center, Peking University, Beijing, China;Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China;

关键词: ischemic stroke;    atrial fibrillation;    deep learning;    fundus image;    multi-spectrum;   

DOI  :  10.3389/fcvm.2023.1185890

 received in 2023-03-17, accepted in 2023-06-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundIschemic stroke (IS) is one of the most common serious secondary diseases of atrial fibrillation (AF) within 1 year after its occurrence, both of which have manifestations of ischemia and hypoxia of the small vessels in the early phase of the condition. The fundus is a collection of capillaries, while the retina responds differently to light of different wavelengths. Predicting the risk of IS occurring secondary to AF, based on subtle differences in fundus images of different wavelengths, is yet to be explored. This study was conducted to predict the risk of IS occurring secondary to AF based on multi-spectrum fundus images using deep learning.MethodsA total of 150 AF participants without suffering from IS within 1 year after discharge and 100 IS participants with persistent arrhythmia symptoms or a history of AF diagnosis in the last year (defined as patients who would develop IS within 1 year after AF, based on fundus pathological manifestations generally prior to symptoms of the brain) were recruited. Fundus images at 548, 605, and 810 nm wavelengths were collected. Three classical deep neural network (DNN) models (Inception V3, ResNet50, SE50) were trained. Sociodemographic and selected routine clinical data were obtained.ResultsThe accuracy of all DNNs with the single-spectral or multi-spectral combination images at the three wavelengths as input reached above 78%. The IS detection performance of DNNs with 605 nm spectral images as input was relatively more stable than with the other wavelengths. The multi-spectral combination models acquired a higher area under the curve (AUC) scores than the single-spectral models.ConclusionsThe probability of IS secondary to AF could be predicted based on multi-spectrum fundus images using deep learning, and combinations of multi-spectrum images improved the performance of DNNs. Acquiring different spectral fundus images is advantageous for the early prevention of cardiovascular and cerebrovascular diseases. The method in this study is a beneficial preliminary and initiative exploration for diseases that are difficult to predict the onset time such as IS.

【 授权许可】

Unknown   
© 2023 Li, Gao, Song, Wu, Li, Cui, Li, Xie, Ren and Zhang.