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

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收录情况： ◇ SCIE

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机构： [1]Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China. [2]Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China. [3]Shenzhen Bay Laboratory, Institute of Biomedical Engineering, Shenzhen, China. [4]National Biomedical Imaging Center, Peking University, Beijing, China. [5]Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China. [6]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China. [7]Department of Cardiology, Beijing Yanhua Hospital, Beijing, China. [8]Cardio-Metabolic Medicine Center, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

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

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Ischemic 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.A 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.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 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.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 that are difficult to predict the onset time such as IS.© 2023 Li, Gao, Song, Wu, Li, Cui, Li, Xie, Ren and Zhang.

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出版当年[2022]版：

大类 | 3 区医学

小类 | 3 区心脏和心血管系统

最新[2023]版：

大类 | 3 区医学

小类 | 3 区心脏和心血管系统

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出版当年[2021]版：

Q2 CARDIAC & CARDIOVASCULAR SYSTEMS

最新[2023]版：

Q2 CARDIAC & CARDIOVASCULAR SYSTEMS

影响因子： 2.8 最新[2023版] 3.1 最新五年平均 5.848 出版当年[2021版] 6.221 出版当年五年平均 6.05 出版前一年[2020版] 3.6 出版后一年[2022版]

第一作者：

第一作者机构： [1]Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China. [2]Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China. [3]Shenzhen Bay Laboratory, Institute of Biomedical Engineering, Shenzhen, China. [4]National Biomedical Imaging Center, Peking University, Beijing, China. [5]Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China.

通讯作者：

通讯机构： [1]Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China. [2]Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China. [3]Shenzhen Bay Laboratory, Institute of Biomedical Engineering, Shenzhen, China. [4]National Biomedical Imaging Center, Peking University, Beijing, China. [5]Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China.

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

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