Artificial intelligence differentiates abdominal Henoch-Schönlein purpura from acute appendicitis in children.

OBJECTIVE

This study aims to construct an artificial intelligence (AI) model capable of effectively discriminating between abdominal Henoch-Schönlein purpura (AHSP) and acute appendicitis (AA) in pediatric patients.

METHODS

A total of 6965 participants, comprising 2201 individuals with AHSP and 4764 patients with AA, were enrolled in the study. Additionally, 53 laboratory indicators were taken into consideration. Five distinct artificial intelligence (AI) models were developed employing machine learning algorithms, namely XGBoost, AdaBoost, Gaussian Naïve Bayes (GNB), MLPClassifier (MLP), and support vector machine (SVM). The performance of these prediction models was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve assessment, and decision curve analysis (DCA).

RESULTS

We identified 32 discriminative indicators (p < .05) between AHSP and AA. Five indicators, namely the lymphocyte ratio (LYMPH ratio), eosinophil ratio (EO ratio), eosinophil count (EO count), neutrophil ratio (NEUT ratio), and C-reactive protein (CRP), exhibited strong performance in distinguishing AHSP from AA (AUC ≥ 0.80). Among the various prediction models, the XGBoost model displayed superior performance evidenced by the highest AUC (XGBoost = 0.895, other models < 0.89), accuracy (XGBoost = 0.824, other models < 0.81), and Kappa value (XGBoost = 0.621, other models < 0.60) in the validation set. After optimization, the XGBoost model demonstrated remarkable diagnostic performance for AHSP and AA (AUC > 0.95). Both the calibration curve and decision curve analysis suggested the promising clinical utility and net benefits of the XGBoost model.

CONCLUSION

The AI-based machine learning model exhibits high prediction accuracy and can differentiate AHSP and AA from a data-driven perspective.