Translational computerized clinical decision support systems for Alzheimer's disease: A systematic review.

Journal: Journal of Alzheimer's disease : JAD
Published Date: May 25, 2025

Abstract

BackgroundAlzheimer's disease (AD), marked by progressive memory loss and cognitive decline, poses diagnostic challenges due to its multifactorial nature. Therefore, researchers are increasingly leveraging artificial intelligence and data-driven approaches to develop computerized clinical decision support systems (CCDSS), aiming to enhance early detection, improve treatment, and slow disease progression.ObjectiveThis study seeks to conduct a systematic review of the most recently developed AD-CCDSS, delving into their progress and the challenges to guide future development and implementation of CCDSS for AD-related decision-making and intervention strategies.MethodsWe follow the PRISMA 2020 guideline to search for articles published within the past seven years across PubMed, ScienceDirect, IEEE Xplore Digital Library, Web of Science, and Scopus, with Google Scholar as a supplementary source. Key components are then extracted from the selected studies for qualitative analysis, including data modalities, computational modeling approaches, system explainability and interpretability, research priorities, and graphical user interfaces designed for non-technical stakeholders.ResultsAfter searching and removing duplicates, we meticulously selected 55 studies. After reviewing key components of CCDSS, we highlight advancements and potential clinical applications, demonstrating their promise in enhancing decision support. However, despite growing attention to explainability in AD-CCDSS, its clinical applicability remains limited. Moreover, challenges such as multi-center system interoperability and data security remain underexplored, hindering real-world implementation.ConclusionsThis study analyzes recent translational AD-CCDSS, identifying key challenges in advancing CCDSS for clinical applications. It offers insights for researchers to enhance CCDSS development and facilitate their integration into clinical practice.

Authors

  • Pinya Lu
    Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application, Fujian Provincial University Engineering Research Center for Big Data Analysis and Application, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China.
  • Mingfeng Chen
    Department of Neurology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
  • Lili Chen
    School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China; School of Chongqing Key Laboratory of Urban Rail Transit Vehicle System Integration and Control, Chongqing Jiaotong University, Chongqing 400074, China.
  • Fan Lin
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Hongqin Yang
    Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China.
  • Yuhua Wang
    International Language School, Huang Huai University, Kai Yuan Road, 76, Zhumadian, 463000, China. yuhuawang6@yahoo.com.
  • Xuemei Ding
    Department of Surgery, Affiliated Hospital of Qingdao University, Qingdao, China.

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