Early detection of dementia through retinal imaging and trustworthy AI

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Jinkui Hao, William R. Kwapong, Ting Shen, Huazhu Fu, Yanwu Xu, Qinkang Lu, Shouyue Liu, Jiong Zhang, Yonghuai Liu, Yifan Zhao, Yalin Zheng, Alejandro F. Frangi, Shuting Zhang, Hong Qi, Yitian Zhao
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引用次数: 0

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 Image

Abstract Image

通过视网膜成像和可信赖的人工智能及早发现痴呆症
阿尔茨海默病(AD)是一项全球性的医疗挑战,目前缺乏一种简单、经济实惠的检测方法。我们提出了一种新型深度学习框架 Eye-AD,利用视网膜微血管和绒毛膜的 OCTA 图像检测早发性阿尔茨海默病(EOAD)和轻度认知障碍(MCI)。Eye-AD 采用多层次图表示法来分析视网膜层的内部和实例间关系。利用一项多中心研究中 1671 名参与者的 5751 张 OCTA 图像,我们的模型在 EOAD(内部数据:AUC = 0.9355,外部数据:AUC = 0.9007)和 MCI 检测(内部数据:AUC = 0.8630,外部数据:AUC = 0.8037)中表现出卓越的性能。此外,我们还探讨了 OCTA 图像中视网膜结构生物标记物与 EOAD/MCI 之间的关联,结果与深度学习可解释性分析得出的结论非常吻合。我们的研究结果进一步证明,视网膜 OCTA 成像与人工智能相结合,将成为一种快速、无创、经济的痴呆症检测方法。
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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