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TRV-2026-0236Certified recordPeer-reviewed

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…

Health · G Space — documented gain · certified 2026-07-17 · v1 · article view · machine-readable

Current reading — gain

Deep learning CT segmentation using Comp2Comp enabled compartment-specific measurement of visceral adipose tissue, subcutaneous fat, and muscle, revealing sustained visceral fat reduction after metabolic and bariatric surgery that BMI alone does not capture.

What this doesn’t fix

Findings are proof-of-concept from a retrospective single-center analysis with a small longitudinal cohort, and prospective validation is still needed to link VAT metrics to cardiometabolic outcomes.

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Truvace Impact Record TRV-2026-0236, v1: “Beyond BMI: Deep Learning Segmentation-Driven CT Reveals Body Composition Changes after Metabolic and Bariatric Surgery.” Truvace, 2026-07-17. /record/TRV-2026-0236 (accessed at citation time). sha256 9244019074297695…

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