Machine learning-based prediction of prolonged air leak after uniportal video-assisted thoracic surgery segmentectomy - Summary - MDSpire
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Predicting Prolonged Air Leak Post-Uniportal Video-Assisted Thoracic Surgery Segmentectomy Using Machine Learning Techniques

  • By

  • Liang Chen

  • Ting Yu

  • Yanqing Pan

  • Guodong Ma

  • June 19, 2026

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Objective:

To develop and internally validate machine learning models for predicting prolonged air leak (PAL) after uniportal video-assisted thoracic surgery (uVATS) segmentectomy.

Approach:
    Key Findings:
    • The XGBoost model achieved the highest AUC of 0.874 [95% confidence interval (CI): 0.833–0.906] in the internal test set.
    Interpretation:

    Machine learning models, particularly XGBoost, show promising internal performance for predicting PAL after uVATS segmentectomy.

    Limitations:
    • The study is based on a single-center retrospective design.
    • External validation and prospective clinical evaluation are necessary before routine clinical implementation.
    Conclusion:

    The study provides evidence-based insights for perioperative risk stratification and individualized care strategies.

Original Source(s)

Machine learning-based prediction of prolonged air leak after uniportal video-assisted thoracic surgery segmentectomy

  • by Liang Chen, Ting Yu, Yanqing Pan, Guodong Ma

  • June 19, 2026

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