SynthesisFrontiers in neurology
Stage-stratified benefits of AI-radiomics PET in early Alzheimer's disease: a systematic review and meta-analysis.
Synthesis in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Objective: AI-radiomics can analyze radiological images more thoroughly and quickly than the human eye. This study aims to compare the diagnostic efficacy of AI-assisted PET radiomics for Alzheimer's disease (AD) with conventional PET diagnosis through a systematic review and bivariate meta-analysis performing indirect, study-level benchmarking versus conventional PET. Methods: PubMed, Embase, and Web of Science were searched through April 11, 2025, for human diagnostic accuracy studies for AI-assisted PET radiomics. Two reviewers extracted data per PRISMA guidelines, risk and bias were appraised using QUADAS-AI. Effect sizes were synthesized via a bivariate random-effects model with HSROC. Prespecified strata contrasted with AD vs. healthy controls (HC), AD vs. mild cognitive impairment (MCI), and tracer class. The analyses were conducted based on bivariate random-effects model realized using R and Stata. Results: Nine studies (25 2 × 2 tables; Conclusion: AI-radiomics on proteinopathy PET shows promising potential for distinguishing AD from MCI, yet only marginal benefits comparing AD to HC. However, given the heterogeneity of the data, the risk of bias, and the limited external validation, there is a need to prioritize multi-site validation, standardized reporting, and prospective decision-impact studies. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251029823, identifier, PROSPERO (CRD420251029823).
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