Skip to main content

REVIEW article

Front. Big Data
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/fdata.2025.1515341
This article is part of the Research Topic Health Data Science and AI in Neuroscience & Psychology View all 3 articles

A review of AI-based radiogenomics in neurodegenerative disease

Provisionally accepted
Qian Liu Qian Liu 1,2*Huanjing Liu Huanjing Liu 1Xiao Zhang Xiao Zhang 2
  • 1 The Department of Applied Computer Science, University of Winnipeg, Winnipeg, Canada
  • 2 Department of Biochemistry and Medical Genetics, Max Rady College of Medicine ,University of Manitoba, Winnipeg, Manitoba, Canada

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

    Neurodegenerative diseases are chronic, progressive conditions that cause irreversible damage to the nervous system, particularly in aging populations. Early diagnosis is a critical challenge, as these diseases often develop slowly and without clear symptoms until significant damage has occurred. Recent advances in radiomics and genomics have provided valuable insights into the mechanisms of these diseases by identifying specific imaging features and genomic patterns.Radiogenomics enhances diagnostic capabilities by linking genomics with imaging phenotypes, offering a more comprehensive understanding of disease progression. The growing field of artificial intelligence (AI), including machine learning and deep learning, opens new opportunities for improving the accuracy and timeliness of these diagnoses. This review examines the application of AI-based radiogenomics in neurodegenerative diseases, summarizing key model designs, performance metrics, publicly available data resources, significant findings, and future research directions. It provides a starting point and guidance for those seeking to explore this emerging area of study.

    Keywords: radiogenomics, neurodegenerative disease, artificial intelligence, Medical image, multi-omics, deep learning

    Received: 22 Oct 2024; Accepted: 31 Jan 2025.

    Copyright: © 2025 Liu, Liu and Zhang. 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: Qian Liu, The Department of Applied Computer Science, University of Winnipeg, Winnipeg, Canada

    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.