Frontiers in surgery · 2026
Development of an automated machine learning-based risk prediction and decision support system for postoperative cubitus varus complicating pediatric lateral humeral condyle fracture.
СтатьяНазвание и аннотация: Перевод в очереди
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Wan Yu, Deng Kun, Yuan Yang, Zhang Li, Li Cong, Cai Haoqi, Wang Yufeng
Аннотация
PURPOSE: Postoperative cubitus varus is a disabling complication of pediatric lateral humeral condyle fractures that impairs long-term elbow function. This study aimed to develop and validate the first Improved LangEvin Equation-based Evolutionary (ILEE) automated machine learning (AutoML) model and visual decision support system for preoperative prediction of this complication, to enable personalized risk stratification and targeted intervention.
METHODS: We conducted a retrospective cohort study of 330 children with lateral humeral condyle fractures treated between January 2005 and June 2022. An ILEE-optimized AutoML model was constructed and compared with the original LEE algorithm and 6 conventional machine learning models. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, and specificity. Key predictors were identified and interpreted via SHapley Additive exPlanations (SHAP) analysis, and a clinical decision support system was developed using MATLAB App Designer.
RESULTS: The ILEE algorithm outperformed all comparators in optimization stability and convergence speed. The AutoML model identified five key features for predicting cubitus varus and exhibited better prediction calibration performance compared to other models (ROC-AUC = 0.9557). SHAP analysis ranked the importance of these features as follows: obesity degree, preoperative timing, internal fixation time, fracture type, and external fixation time. The decision support system can generate real-time risk levels, predicted probabilities, and personalized clinical recommendations within 1 s.
CONCLUSION: This study establishes the first ILEE-based AutoML model for predicting postoperative cubitus varus in pediatric lateral humeral condyle fractures, demonstrating competitive predictive performance. The user-friendly visual system enables rapid preoperative risk assessment, assisting clinicians in identify high-risk patients, optimize treatment plans, and potentially improving pediatric orthopedic outcomes.
METHODS: We conducted a retrospective cohort study of 330 children with lateral humeral condyle fractures treated between January 2005 and June 2022. An ILEE-optimized AutoML model was constructed and compared with the original LEE algorithm and 6 conventional machine learning models. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, and specificity. Key predictors were identified and interpreted via SHapley Additive exPlanations (SHAP) analysis, and a clinical decision support system was developed using MATLAB App Designer.
RESULTS: The ILEE algorithm outperformed all comparators in optimization stability and convergence speed. The AutoML model identified five key features for predicting cubitus varus and exhibited better prediction calibration performance compared to other models (ROC-AUC = 0.9557). SHAP analysis ranked the importance of these features as follows: obesity degree, preoperative timing, internal fixation time, fracture type, and external fixation time. The decision support system can generate real-time risk levels, predicted probabilities, and personalized clinical recommendations within 1 s.
CONCLUSION: This study establishes the first ILEE-based AutoML model for predicting postoperative cubitus varus in pediatric lateral humeral condyle fractures, demonstrating competitive predictive performance. The user-friendly visual system enables rapid preoperative risk assessment, assisting clinicians in identify high-risk patients, optimize treatment plans, and potentially improving pediatric orthopedic outcomes.