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ORIGINAL RESEARCH article

Front. Public Health
Sec. Digital Public Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1449798
This article is part of the Research Topic AI-Driven Healthcare Delivery, Ageism, and Implications for Older Adults: Emerging Trends and Challenges in Public Health View all 6 articles

ReIU: An Efficient Preliminary Framework for Alzheimer Patients based on Multi-model Data

Provisionally accepted
Hao Jiang Hao Jiang 1*Yishan Qian Yishan Qian 2Liqiang Zhang Liqiang Zhang 1*Tao Jiang Tao Jiang 1*Yonghang Tai Yonghang Tai 1*
  • 1 Yunnan Normal University, Kunming, China
  • 2 Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, Shanghai Municipality, China

The final, formatted version of the article will be published soon.

    The rising incidence of Alzheimer's disease (AD) poses significant challenges to traditional diagnostic methods, which primarily rely on neuropsychological assessments and brain MRIs. The advent of deep learning in medical diagnosis opens new possibilities for early AD detection. In this study, we introduce retinal vessel segmentation methods based on U-Net ad iterative registration Learning (ReIU), which extract retinal vessel maps from OCT angiography (OCT-A) facilities. Our method achieved segmentation accuracies of 79.1% on the DRIVE dataset, 68.3% on the HRF dataset. Utilizing a multimodal dataset comprising both healthy and AD subjects, ReIU extracted vascular density from fundus images, facilitating primary AD screening with a classification accuracy of 79%. These results demonstrate ReIU's substantial accuracy and its potential as an economical, non-invasive screening tool for Alzheimer's disease. This study underscores the importance of integrating multi-modal data and deep learning techniques in advancing the early detection and management of Alzheimer's disease.

    Keywords: Alzheimer Patients Multimodal Data, Retinal vessel segmentation, biomarker extraction, Preliminary Patients Screening, deep learning

    Received: 16 Jun 2024; Accepted: 10 Dec 2024.

    Copyright: © 2024 Jiang, Qian, Zhang, Jiang and Tai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hao Jiang, Yunnan Normal University, Kunming, China
    Liqiang Zhang, Yunnan Normal University, Kunming, China
    Tao Jiang, Yunnan Normal University, Kunming, China
    Yonghang Tai, Yunnan Normal University, Kunming, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.