Wang Zhen, Li Mingxiao, Xia Peng, Jiang Chao, Shen Ting, Ma Jiaming, Bai Yu, Zhang Suhui, Lai Yiwei, Li Sitong, Xu Hui, Xu Yang, Ma Tong, Ju Lie, He Liu, Dong Li, Sang Caihua, Long Deyong, Chen Yuzhong, Du Xin, Ge Zongyuan, Dong Jianzeng, Wei Wen-Bin, Ma Changsheng
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Engineering Research Center of Medical Devices for Cardiovascular Diseases, Ministry of Education, Capital Medical University, Beijing, China.
Heart Rhythm O2. 2025 Feb 7;6(5):678-686. doi: 10.1016/j.hroo.2025.01.019. eCollection 2025 May.
Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients.
Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients.
From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated.
A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816-0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823-0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709-0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc.
A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients.
心房颤动（AF）患者发生认知障碍（CI）的风险较高。然而，CI诊断的复杂性以及缺乏简单的筛查方法限制了AF患者CI的早期筛查和干预。
我们的研究旨在基于眼底照片开发深度学习模型，以便于对AF患者进行CI筛查。
纳入2021年5月至2023年4月在中国心房颤动注册研究中完成眼底检查和认知功能评估的患者。训练集和验证集按8:2的比例随机划分。来自北京眼病研究的参与者作为外部验证集。训练了不同的深度学习模型，并验证了它们的CI检测能力。
中国心房颤动注册研究共纳入899例患者。在验证集中，仅基于眼底图像的视觉集成模型用于CI筛查时，受试者操作特征曲线下面积（AUROC）为0.855（95%置信区间0.816-0.894）。基于眼底照片和4个临床变量的多模态模型（AUROC 0.861，95%置信区间0.823-0.898）与视觉集成模型表现相当。视觉集成模型在外部测试集中用于CI筛查的AUROC为0.773（95%置信区间0.709-0.837）。在显著性图中，视觉集成模型聚焦于视网膜血管和视盘周围区域。
基于眼底图像的视觉集成模型可能适用于AF患者CI的初步筛查。
Zotero 插件
