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Early detection of dementia through retinal imaging and trustworthy AI.

Authors :
Hao, Jinkui
Kwapong, William R.
Shen, Ting
Fu, Huazhu
Xu, Yanwu
Lu, Qinkang
Liu, Shouyue
Zhang, Jiong
Liu, Yonghuai
Zhao, Yifan
Zheng, Yalin
Frangi, Alejandro F.
Zhang, Shuting
Qi, Hong
Zhao, Yitian
Source :
NPJ Digital Medicine; 10/20/2024, Vol. 7 Issue 1, p1-15, 15p
Publication Year :
2024

Abstract

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23986352
Volume :
7
Issue :
1
Database :
Complementary Index
Journal :
NPJ Digital Medicine
Publication Type :
Academic Journal
Accession number :
180372517
Full Text :
https://doi.org/10.1038/s41746-024-01292-5