An Integrated Risk Prediction Model for Gout Using Clinical Data, Ultrasound Features, and Deep Learning: A Retrospective Multicenter Study.

Purpose: To develop and validate a combined model for predicting gout risk by integrating ultrasound (US) features as novel risk factors with clinical data and predictions from deep learning (DL) models. Patients and Methods: This retrospective study included 609 cases who underwent first metatarsophalangeal (MTP1) joint US at two centers. Data from Center 1 were divided into a training group (70%, n = 355) and an internal testing cohort (ITC) (30%, n = 162). Data from Center 2 served as an external testing cohort (ETC) (n = 92). A DL diagnostic model based on MTP1 US images was developed to obtain diagnostic predictions. Clinical data, US features, and DL predictions were integrated, and logistic regression analysis was performed to identify independent risk factors. Various models were constructed (clinical, US, clinical-US, clinical-DL, and combined), and the best model was interpreted with a nomogram. Multicollinearity was assessed using the variance inflation factor. Model performance was evaluated using the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results: The combined model, incorporating clinical data (gender, serum uric acid [SUA]), US features (tophus, double contour sign (DCs), bone erosion), and DL predictions, exhibited the best performance. For the ITC, the area under the curve (AUC) and Brier scores were 0.904 (95% CI: 0.843~0.965) and 0.100 (0.066~0.140), respectively. For the ETC, they were 0.881 (95% CI: 0.815~0.947) and 0.160 (0.107~0.221). DCA confirmed the clinical utility of the combined nomogram. Conclusion: A nomogram was constructed based on seven risk predictors (gender, SUA, estimated glomerular filtration rate (eGFR), tophus, bone erosion, DCs, and DL prediction) to predict and quantify gout risk.