JMIR Publications

ABSTRACT

Background:

Neuroimaging is crucial in the diagnosis of Alzheimer's disease (AD). In recent years, artificial intelligence (AI) based neuroimaging technology has rapidly developed, providing new methods for accurate diagnosis of AD, but its performance differences still need to be systematically evaluated.

Objective:

To conduct a systematic review and meta-analysis comparing the diagnostic performance of AI-assisted fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) and structural magnetic resonance imaging (sMRI) for AD.

Methods:

Databases including Web of Science, PubMed, and Embase were searched from inception to January 2025 to identify original studies developing or validating AI models for AD diagnosis using ¹⁸F-FDG PET or sMRI. Methodological quality was assessed using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-Artificial Intelligence（TRIPOD-AI) checklist. A bivariate mixed-effects model was employed to calculate pooled sensitivity, specificity, and summary receiver operating characteristic curve area (SROC-AUC).

Results:

38 studies were included, with 28 moderate-to-high-quality studies analyzed. Pooled SROC-AUC values were 0.94 (95% CI: 0.92–0.96) for sMRI and 0.96 (95% CI: 0.94–0.98) for ¹⁸F-FDG PET, demonstrating statistically significant intermodal differences (P=.02). Subgroup analyses revealed that in machine learning (ML), pooled SROC-AUCs were 0.89 (95% CI: 0.86–0.92) for sMRI and 0.95 (95% CI: 0.92–0.96) for ¹⁸F-FDG PET, while in deep learning (DL), these values were 0.96 (95% CI: 0.94–0.97) and 0.97 (95% CI: 0.96–0.99), respectively. Meta-regression identified heterogeneity arising from study quality stratification, algorithm types, and validation strategies.

Conclusions:

Both AI-assisted 18F-FDG PET and sMRI exhibit high diagnostic accuracy in AD, with 18F-FDG PET demonstrating superior overall diagnostic performance compared to sMRI.