Association and Discrimination of Urinary System Stone Disease Using Anthropometric Indices, Lifestyle Behaviors, and Artificial Intelligence: A Multicenter Case–Control Study

Background/Objectives: Urinary system stone disease is common and highly recurrent, and body mass index (BMI) poorly reflects the central adiposity linked to stone risk. We compared BMI with five next-generation anthropometric indices (A Body Shape Index [ABSI], Body Roundness Index [BRI], Waist-to-Height Ratio [WtHR], Conicity Index [ConI], Waist-to-Hip Ratio [WHR]) for their association with stone disease, and developed a hybrid autoencoder-based artificial intelligence model integrating anthropometric, clinical, and lifestyle data. Methods: This multicenter case–control study included 1195 participants (562 stone cases, 633 controls) from six centers in Turkey. Thirty-one variables were grouped into three subgroups (basic measurements, derived indices, clinical/lifestyle data), each processed by a separate autoencoder; combined features were classified with XGBoost, Cubic SVM, Fine KNN, and logistic regression. Each index’s independent association was assessed by quartile-based logistic regression, and discriminative power by ROC/AUC. Results: BMI showed no between-group difference (p = 0.919) or discriminative power (AUC = 0.498). ABSI, BRI, WtHR, ConI, and WHR were all significantly higher in the stone group, showed positive trends across quartiles after full adjustment (all p-trend ≤ 0.001), and outperformed BMI (AUC 0.547–0.564); associations were strongest for ConI (aOR = 2.62) and ABSI (aOR = 2.57). The hybrid AI model performed best with XGBoost (77% accuracy), followed by Fine KNN (71%), logistic regression (69%), and Cubic SVM (68%). Conclusions: Unlike BMI, next-generation indices reflecting central adiposity are independently and consistently associated with stone disease, and a multivariable AI model combining anthropometric, clinical, and lifestyle data reached 77% accuracy in separating stone patients from controls on a single test split. These findings are a hypothesis-generating risk-stratification signal that requires external validation, and they do not support clinical screening.