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Diagnostic Accuracy of MRI-Based Artificial Intelligence Models for Distinguishing Alzheimer's Disease and Mild
1Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Abstract:
Magnetic resonance imaging (MRI)-based artificial intelligence (AI) models are increasingly applied to brain MRI for diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI), but their overall diagnostic performance remains unclear. We systematically searched PubMed/MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore until February 15, 2026, for diagnostic accuracy studies of machine-learning or deep-learning models using structural brain MRI to distinguish AD vs. cognitively normal (CN) controls and MCI or late mild cognitive impairment (LMCI) vs. CN controls. Seven studies met inclusion criteria, contributing five AD vs. CN and two MCI/LMCI vs. CN tasks, predominantly using deep-learning architectures applied to Alzheimer's Disease Neuroimaging Initiative cohorts. For AD vs. CN (five studies), pooled sensitivity was 0.96 (95% confidence interval [CI], 0.93-0.97) and pooled specificity was 0.95 (95% CI, 0.92-0.97), indicating excellent discrimination. For MCI/LMCI vs. CN (two studies), sensitivity was consistently high (0.91-0.93), whereas specificity varied widely (0.54-0.98), limiting the interpretability of pooled estimates. MRI-based AI models therefore show strong performance for established AD but heterogeneous specificity for MCI, underscoring the need for larger, externally validated studies in diverse populations.
Insights
Artificial intelligence (AI) using magnetic resonance imaging (MRI) shows excellent accuracy for diagnosing Alzheimer's disease (AD). However, AI's specificity for mild cognitive impairment (MCI) varies, requiring further validation in diverse populations.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Neurology
Background:
- Artificial intelligence (AI) models are increasingly utilized for diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI) using magnetic resonance imaging (MRI).
- The diagnostic performance of these AI models requires systematic evaluation to understand their clinical utility.
- Distinguishing between AD, MCI, and cognitively normal (CN) individuals is crucial for timely intervention and management.
Purpose of the Study:
- To systematically review and synthesize the diagnostic accuracy of AI models applied to brain MRI for differentiating AD vs. CN and MCI/LMCI vs. CN.
- To assess the pooled sensitivity and specificity of these AI models across included studies.
- To identify limitations and areas for future research in AI-based neuroimaging diagnostics.
Main Methods:
- A systematic literature search was conducted across major databases (PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore) up to February 15, 2026.
- Included studies focused on diagnostic accuracy of machine-learning or deep-learning models using structural brain MRI.
- Seven studies met the inclusion criteria, analyzing data predominantly from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts.
Main Results:
- For AD vs. CN classification, AI models demonstrated excellent diagnostic performance with pooled sensitivity of 0.96 (95% CI, 0.93-0.97) and pooled specificity of 0.95 (95% CI, 0.92-0.97).
- For MCI/LMCI vs. CN classification, sensitivity remained high (0.91-0.93), but specificity showed significant variability (0.54-0.98), impacting the reliability of pooled estimates.
- The majority of included studies employed deep-learning architectures.
Conclusions:
- MRI-based AI models exhibit strong diagnostic capabilities for established Alzheimer's disease.
- The specificity of AI models for detecting mild cognitive impairment is heterogeneous, necessitating cautious interpretation.
- Larger, externally validated studies incorporating diverse populations are essential to enhance the reliability and generalizability of AI in neurodegenerative disease diagnosis.
