Research on Automatic Diagnosis of Alzheimer's Disease Neuroimaging and Prediction of Disease Progression

Zeyu Xu

2025

Abstract

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, represents a significant global health challenge, with early diagnosis being pivotal for mitigating cognitive decline. Traditional diagnostic methods, mainly relying on subjective neuroimaging evaluations like sMRI and PET, are afflicted by inter-rater inconsistencies and limited sensitivity to preclinical biomarkers such as β-amyloid plaques.This review synthesizes existing research on deep learning (DL) techniques for automated AD diagnosis and progression prediction. When applied to multi-modal datasets such as ADNI and OASIS, convolutional neural networks (CNNs) and Transformers have shown notable effectiveness. Evidently, traditional machine learning models, including support vector machines (SVM) and random forests (RF), generally attain an accuracy of 85%–88% through multi-modal feature fusion. In contrast, DL frameworks, by capturing subtle brain alterations like insular cortex atrophy, can achieve accuracies surpassing 93%.However, prevalent issues across these studies—data scarcity, underrepresentation of early-stage cases, and low model interpretability—remain. Future directions should emphasize federated learning for data integration, development of hybrid neuroimaging-multi-omics models, and advancement of explainable AI, all aimed at facilitating clinical translation.

Paper Citation

in Harvard Style

Xu Z. (2025). Research on Automatic Diagnosis of Alzheimer's Disease Neuroimaging and Prediction of Disease Progression. In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-792-4, SciTePress, pages 594-598. DOI: 10.5220/0014367100004718

in Bibtex Style

@conference{emiti25,
author={Zeyu Xu},
title={Research on Automatic Diagnosis of Alzheimer's Disease Neuroimaging and Prediction of Disease Progression},
booktitle={Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2025},
pages={594-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014367100004718},
isbn={978-989-758-792-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Research on Automatic Diagnosis of Alzheimer's Disease Neuroimaging and Prediction of Disease Progression
SN - 978-989-758-792-4
AU - Xu Z.
PY - 2025
SP - 594
EP - 598
DO - 10.5220/0014367100004718
PB - SciTePress