Beyond BMI: Deep Learning Segmentation-Driven CT Reveals Body Composition Changes After Metabolic and Bariatric Surgery.

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Abstract

Body mass index (BMI) is the primary metric used to evaluate metabolic and bariatric surgery (MBS) outcomes but does not distinguish tissue compartments or quantify visceral adiposity (VAT), a key determinant of cardiometabolic risk. We evaluated the relationship between BMI and VAT and characterized compartment-specific remodeling following MBS using artificial intelligence-enabled CT segmentation. A retrospective analysis of prospectively collected abdominal CT scans was performed at a single tertiary center. Images were processed using Comp2Comp, a validated deep-learning pipeline for automated segmentation of visceral adipose tissue, subcutaneous adipose tissue, and skeletal muscle. A population cohort of 435 adults with BMI ≥25 kg/m² undergoing CT for clinical indications was analyzed to assess baseline BMI-VAT associations. A longitudinal MBS cohort (n=39 with complete follow-up; 151 CT studies; follow-up to 89 months) was evaluated for temporal changes in BMI, VAT, and muscle. In the population cohort, VAT was moderately correlated with BMI (r=0.36, p<0.001); however, this association was not significant among patients with BMI ≥35 kg/m² (r=0.10, p=0.37). In the MBS cohort, BMI and VAT demonstrated weak correlation (R²=0.237, p<0.001). Postoperatively, VAT reduction showed a stronger temporal association (R²=0.661, p<0.001) than BMI decline (R²=0.571, p<0.001), with BMI plateauing at longer follow-up. Skeletal muscle demonstrated a distinct recovery trajectory (R²=0.551, p<0.001). BMI incompletely reflects postoperative tissue remodeling, particularly sustained VAT reduction, after MBS. AI-enabled CT volumetric analysis demonstrates proof-of-concept for compartment-specific assessment beyond BMI. Prospective validation is required to determine whether VAT-derived metrics more accurately predict cardiometabolic outcomes.

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