The Journal of Practical Medicine ›› 2025, Vol. 41 ›› Issue (24): 3929-3940.doi: 10.3969/j.issn.1006-5725.2025.24.018

• Medical Examination and Clinical Diagnosis • Previous Articles    

An interpretable machine learning model based on bedside lung and diaphragm ultrasound for preoperative prediction of pulmonary dysfunction in gastrointestinal tumor surgery： A clinical study

Tianyuan LI,Ying TIAN,Dingde LONG,Yang DONG,Huan. FU()   

  1. Department of Anesthesiology，the First Affiliated Hospital of Nanchang University，Nanchang 330006，Jiangxi，China
  • Received:2025-10-16 Online:2025-12-25 Published:2025-12-25
  • Contact: Huan. FU E-mail:ndyfy06109@ncu.edu.cn

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Abstract:

Objective To develop and validate an interpretable machine learning model based on bedside lung and diaphragm ultrasound for preoperative prediction of pulmonary dysfunction in patients undergoing gastrointestinal tumor surgery. Methods In this prospective study， data from 424 patients （June 2021?December 2023） were used for model development， with external validation conducted on an independent cohort of 101 patients （January 2024-December 2024）. Clinical variables， PFTs results， and ultrasound parameters （LUS score， diaphragmatic excursion， and thickening fraction） were collected. Three feature selection methods-Least Absolute Shrinkage and Selection Operator （LASSO）， Support Vector Machine Recursive Feature Elimination （SVM-RFE）， eXtreme Gradient Boosting Recursive Feature Elimination （XGBoost-RFE）- were employed to identify key predictors. Five machine learning algorithms were trained and evaluated using 5-fold cross-validation. The optimal model was assessed based on the area under the receiver operating characteristic curve （AUC）， accuracy， sensitivity， specificity， calibration， and decision curve analysis. SHapley Additive exPlanations （SHAP） analysis was applied to enhance model interpretability. Results The prevalence of preoperative pulmonary dysfunction was 36.8%. Three key predictors were consistently identified： diaphragmatic excursion during deep breathing ［D-DE （4.26 cm vs. 5.05 cm， P < 0.001）］， LUS score ［LUSs （4 vs. 1， P < 0.001）］， and diaphragmatic thickening fraction during deep breathing ［D-DTF （39.83% vs. 71.80%， P < 0.001）］. The Support Vector Machine （SVM） model demonstrated superior generalizability， achieving an AUC of 0.867 on the internal test set （accuracy of 80.0%， sensitivity of 81.8%， specificity of 79.4%）. External validation confirmed its robust performance， with an AUC of 0.934 （95% CI： 0.881 ~ 0.987） and an accuracy of 88.0%. SHAP analysis revealed D-DTF as the most influential protective factor. Conclusion An interpretable SVM model integrating D-DE， LUSs， and D-DTF accurately predicts preoperative pulmonary dysfunction in patients with gastrointestinal tumors， offering a non-invasive， bedside-compatible new approach for preoperative risk assessment.

Key words: lung ultrasound, diaphragm ultrasound, pulmonary function tests, machine learning, preoperative assessment

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