• Network analysis of optimal de...

*Result*: Network analysis of optimal deep or machine learning strategies for classification and detection of Alzheimer's disease based on MRI scanning.

Title:
Network analysis of optimal deep or machine learning strategies for classification and detection of Alzheimer's disease based on MRI scanning.
Authors:
Zhang Q; Department of Radiology, Shaoxing Seventh People's Hospital, Shaoxing, Zejiang, China., Ma L; Department of Radiology, Affiliated Hospital of Shaoxing University, Shaoxing, Zejiang, China., Zhao L; Department of Radiology, Ningbo Yinzhou District Second Hospital, Ningbo, Zejiang, China., Zhu S; Department of Radiology, Shaoxing Seventh People's Hospital, Shaoxing, Zejiang, China., Qi H; Department of Radiology, Shaoxing Seventh People's Hospital, Shaoxing, Zejiang, China., Pan Z; Department of Radiology, Shaoxing Seventh People's Hospital, Shaoxing, Zejiang, China., Zhou J; Department of Radiology, Shaoxing Seventh People's Hospital, Shaoxing, Zejiang, China.
Source:
Frontiers in neuroscience [Front Neurosci] 2026 Jan 30; Vol. 20, pp. 1644480. Date of Electronic Publication: 2026 Jan 30 (Print Publication: 2026).
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101478481 Publication Model: eCollection Cited Medium: Print ISSN: 1662-4548 (Print) Linking ISSN: 1662453X NLM ISO Abbreviation: Front Neurosci Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Lausanne : Frontiers Research Foundation
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Contributed Indexing:
Keywords: Alzheimer’s disease; MRI scanning; deep learning; machine learning; network meta-analysis
Entry Date(s):
Date Created: 20260216 Date Completed: 20260216 Latest Revision: 20260218
Update Code:
20260218
PubMed Central ID:
PMC12901351
DOI:
10.3389/fnins.2026.1644480
PMID:
41695973
Database:
MEDLINE

*Further Information*

*Background: Alzheimer's disease (AD) presents a significant global health challenge, with its prevalence projected to increase substantially by 2050. Despite its widespread impact, the underlying causes and mechanisms remain incompletely understood, complicating efforts toward effective diagnosis and treatment. Pathologically, AD is marked by the accumulation of senile plaques and neurofibrillary tangles, but the relationship between these factors and disease progression is complex and heterogeneous.
Objective: The present study aimed to compare the efficacy of different deep/machine learning models based on MRI scanning.
Methods: The study follows rigorous systematic review protocols, adhering to the Cochrane Handbook of Systematic Reviews and Interventions and the PRISMA guidelines. A comprehensive search strategy was employed across multiple databases, including PubMed, Web of Science, Cochrane, Medline, and EMBASE. Advanced statistical methods were used for data synthesis and analysis, incorporating network meta-analysis and machine learning techniques to evaluate the accuracy and efficacy of different diagnostic models.
Results: The meta-analysis included 11 studies that met the predefined inclusion criteria. The studies employed various machine learning algorithms, including CNN, ResNet, and DenseNet, to classify AD and distinguish it from mild cognitive impairment (MCI) and healthy controls. The results indicate that CNN and ResNet consistently outperform other models in terms of classification accuracy. Additionally, the integration of nanotechnology and AI-driven diagnostics demonstrates significant potential in enhancing the diagnostic process.
Conclusion: Despite challenges such as data heterogeneity and the interpretability of AI-driven models, the study highlights the transformative potential of computational techniques and advanced imaging technologies in AD diagnosis and management. The integration of network-based analyses and machine learning approaches offers promising avenues for future research, aiming to revolutionize the understanding and approach to Alzheimer's disease.
(Copyright © 2026 Zhang, Ma, Zhao, Zhu, Qi, Pan and Zhou.)*

*The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.*