The Combination of AI-driven Abdominal CT and Inbody Analysis Plays a Complementary Role in Predicting Metabolic Syndrome

Purpose: Metabolic syndrome is a multifactorial condition associated with increased risks of cardiovascular disease and type 2 diabetes. This study aims to evaluate whether combining AI-based abdominal CT metrics with traditional InBody analysis enhances the prediction of metabolic syndrome.Materials and Methods: This retrospective study included 977 adults who underwent both abdominal CT and InBody assessments. AI-derived measurements were obtained using a deep-learning V-Net model trained to segment seven body tissue types. InBody measurements included BMI, body fat percentage, fat mass, and waist-hip ratio. Metabolic syndrome was defined by NCEP-ATP III criteria. Logistic regression and ROC analyses were used to evaluate the predictive performance of AI-derived metrics, InBody metrics, and their combination.Results: Body fat percentage and waist-hip ratio from InBody analysis were strong predictors of metabolic syndrome (AUC 0.82). AI-derived visceral fat was also significantly associated with metabolic syndrome (AUC 0.61). Combining both AI and InBody metrics slightly improved predictive performance (AUC 0.83), indicating a complementary diagnostic value.Conclusions: While InBody metrics remain superior in predicting metabolic syndrome due to their close association with metabolic processes, AI-derived body composition metrics, particularly visceral fat, offer structural insights. The modest improvement in prediction when combined suggests the potential of an integrated diagnostic model in clinical practice.