Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning - Li, Hui; Gao, Mengdi; Song, Haiqing; Wu, Xiao; Li, Gang; Cui, Yiwei; Li, Yang; Xie, Zhaoheng; Ren, Qiushi; Zhang, Haitao (Frontiers in Cardiovascular Medicine, 2023)
Background: Ischemic stroke (IS) is one of the most common serious secondary diseases of atrial fibrillation (AF) in one year, 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. Methods: A total of 150 AF participants without suffering from IS within one 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 one year after AF, based on fundus pathological manifestations generally prior to symptoms of the brain) were recruited. Fundus Images at 548 nm, 605 nm, and 810 nm wavelengths were collected. Three classical deep neural network models (DNNs) (Inception V3, ResNet50, SE50) were trained. Sociodemographic and selected routine clinical data were obtained. Results: The 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 605nm spectral images as input was relatively more stable than with the other wavelengths. The multi-spectral combination models acquired a higher AUC than the single spectral models. Conclusions: The 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 such as IS difficult to predict the onset time.