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MINI REVIEW article

Front. Neurol.
Sec. Applied Neuroimaging
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1536463
This article is part of the Research Topic Frontier Research on Artificial Intelligence and Radiomics in Neurodegenerative Diseases View all 4 articles

From Pixels to Prognosis: Radiomics and AI in Alzheimer's Disease Management

Provisionally accepted
Danting Peng Danting Peng *Weiju Huang Weiju Huang *Ren Liu Ren Liu *Wenlong Zhong Wenlong Zhong *
  • Radiology department，Chonggang General Hospital, Chongqing, China

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

    Alzheimer's disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD pathology. Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. This review explores the application of radiomics and AI in AD, focusing on key imaging modalities such as PET and MRI, as well as multimodal approaches combining structural and functional data. We discuss the potential of these technologies to identify disease-specific biomarkers, predict disease progression, and guide personalized interventions.Additionally, the review addresses critical challenges, including data standardization, model interpretability, and the integration of AI into clinical workflows. By highlighting current achievements and identifying future directions, this article underscores the transformative potential of AI-driven radiomics in reshaping AD diagnostics and care.

    Keywords: Alzheimer's disease, AD, Radiomics, artificial intelligence, deep learning, Neurodegenerative Diseases, biomarkers, Neuroimaging

    Received: 28 Nov 2024; Accepted: 08 Jan 2025.

    Copyright: © 2025 Peng, Huang, Liu and Zhong. 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:
    Danting Peng, Radiology department，Chonggang General Hospital, Chongqing, China
    Weiju Huang, Radiology department，Chonggang General Hospital, Chongqing, China
    Ren Liu, Radiology department，Chonggang General Hospital, Chongqing, China
    Wenlong Zhong, Radiology department，Chonggang General Hospital, Chongqing, 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.