Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis
Abstract
Artificial intelligence (AI) enables automated, high-throughput adiposity quantification, offering refined risk stratification for women with overweight and obesity. We systematically reviewed and meta-analyzed studies evaluating AI-based segmentation of visceral, subcutaneous, and total fat in adult women populations (BMI: 25-29.9 and ≥ 30 kg/m2), searching MEDLINE, CENTRAL, Embase, Scopus, ScienceDirect, and Web of Science from inception to January 15, 2025, in accordance with PRISMA guidelines. Studies were included if they (1) focused on adult women (≥ 18 years) undergoing MRI, CT, or DXA-based fat quantification; (2) employed automated or conventional radiomics segmentation; and (3) reported quantitative adiposity metrics (volumes or areas). Two reviewers independently extracted data, assessed quality via QUADAS-2, and synthesized results using inverse-variance and sample size-weighted random-effects models. Of 13,098 records screened, 38 studies (12,815 women) met inclusion criteria. AI methods demonstrated high segmentation accuracy, exemplified by 3D-Unet (Dice score: 0.97 [95% CI: 0.96-0.98]) and > 99% faster processing than manual methods (0.05 vs. 46.5 min/patient; p 3 (I2 = 99.9%), visceral fat 3615 cm3 (I2 = 100%), and total fat 35,062 cm3 (I2 = 99.8%), with prediction intervals spanning anatomically implausible ranges. This variability stemmed from inconsistent acquisition parameters, segmentation protocols, and modality-specific differences, with DXA-derived…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 12,815 participants or records.
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