Meta analysis of the diagnostic efficacy of transformer-based multimodal fusion deep learning models in early Alzheimer's disease.

Introduction: This study aims to systematically evaluate the diagnostic efficacy of Transformer-based multimodal fusion deep learning models in early Alzheimer's disease (AD) through a Meta-analysis, providing a scientific basis for clinical applications. Methods: Following PRISMA guidelines, databases such as PubMed and Web of Science were searched, and 20 eligible clinical studies (2022-2025) involving 12,897 participants were included. Study quality was assessed using the modified QUADAS-2 tool, statistical analyses were performed with Stata 16.0, effect sizes were pooled via random-effects models, and subgroup analyses, sensitivity analyses, and publication bias tests were conducted. Results: Results showed that Transformer-based multimodal fusion models exhibited excellent overall diagnostic performance, with a pooled AUC of 0.924 (95% CI: 0.912-0.936), sensitivity of 0.887 (0.865-0.904), specificity of 0.892 (0.871-0.910), and accuracy of 0.879 (0.858-0.897), significantly outperforming traditional single-modality methods. Subgroup analyses revealed that: Three or more modalities achieved a higher AUC (0.935 vs. 0.908 for two modalities, Discussion: Notable studies included Khan et al.'s (2024) Dual-3DM