Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.

OBJECTIVES

Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic peripheral neuropathy (DPN), conduct comparative analyses of these models, and further evaluate and validate the diagnostic efficacy of the optimal model for DPN as well as its potential in clinical application.

METHODS

The study included 235 patients grouped according to criteria for diagnosis of DPN. Ultrasound and clinical data were collected concurrently. The dataset was randomly partitioned into training and testing sets at a 7:3 ratio. Four machine learning models were developed: logistic regression (LR), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). These models were evaluated using a 10-fold cross-validation, comparing accuracy and areas under the curve (AUC) to determine the most optimal model. Based on SHAP (Shapley additive explanation) value visualization, decision curve analysis (DCA) and clinical impact curve (CIC) were employed to assess clinical utility.

RESULTS

Comparisons of different models indicate that the RF model performed best overall across all models, reaching an AUC of 0.852 in the test set while also producing the highest recall rates and F1 scores. SHAP analysis revealed key risk factors identified in the RF model, in order of importance: body mass index (BMI), 2-hour C-peptide (2hC-P), diabetes duration, triglycerides (TG), CSA-CPN1, and age. In addition, DCA and CIC demonstrate the model's clinical utility.

CONCLUSIONS

The RF model demonstrated optimal performance for DPN prediction and shows significant potential for clinical application in DPN risk assessment.