Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

Background: 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. Objective: Our study aimed to develop deep learning models ba...

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Main Authors: Zhen Wang, MD, Mingxiao Li, MD, Peng Xia, BS, Chao Jiang, MD, Ting Shen, MD, Jiaming Ma, MD, Yu Bai, MD, Suhui Zhang, MD, Yiwei Lai, MD, Sitong Li, MS, Hui Xu, MD, Yang Xu, MD, Tong Ma, MS, Lie Ju, PhD, Liu He, PhD, Li Dong, MD, Caihua Sang, MD, Deyong Long, MD, Yuzhong Chen, PhD, Xin Du, MD, Zongyuan Ge, PhD, Jianzeng Dong, MD, Wen-Bin Wei, MD, Changsheng Ma, MD
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:Heart Rhythm O2
Subjects:
Cognitive impairment
Atrial fibrillation
Artificial intelligence
Fundus photography
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2666501825000455
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Summary:Background: 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. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: 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. Results: 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. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients.
ISSN:2666-5018

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