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

BACKGROUND: BMI is the primary metric used to evaluate outcomes of metabolic and bariatric surgery (MBS), but it 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 after MBS using artificial intelligence-enabled CT segmentation. STUDY DESIGN: 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 greater than or equal to 25 kg/m 2 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. RESULTS: 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 greater than or equal to 35 kg/m 2 ( r = 0.10, p = 0.37). In the MBS cohort, BMI and VAT demonstrated a 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). CONCLUSIONS: BMI incompletely reflects postoperative tissue remodeling, particularly sustained VAT reduction, after MBS. Artificial intelligence-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.