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The study aims to evaluate the effectiveness of varying machine learning models for diagnosing Alzheimer's disease using MRI data.

February 2, 2026Open Access

Network analysis of optimal deep or machine learning strategies for classification and detection of Alzheimer’s disease based on MRI scanning

Key Points

The study aims to evaluate the effectiveness of varying machine learning models for diagnosing Alzheimer's disease using MRI data.
Conducted a systematic review following Cochrane and PRISMA guidelines.
Performed a comprehensive literature search across multiple databases including PubMed and EMBASE.
Employed network meta-analysis and other statistical techniques to assess model accuracy.
The meta-analysis included 11 relevant studies focusing on Alzheimer's diagnostics.
CNN and ResNet models showed superior performance in accurately classifying Alzheimer's from mild cognitive impairment and healthy controls.
Evidence of nanotechnology and AI integration suggests enhancements in diagnostic accuracy.

Abstract

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.

Network analysis of optimal deep or machine learning strategies for classification and detection of Alzheimer’s disease based on MRI scanning

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