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.

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