ArticleMedComm2026
LungSurg: A Generative AI System for Segmentation and Phase Classification in Thoracoscopic Lobectomy.
Article in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Decoding surgical competence: surgical gesture analysis can determine competence in simulated VATS lobectomy, a validation study.Surgical endoscopy · 2026Article
- Artificial Intelligence and the Ethical Foundations of Cardiothoracic Surgery: Evidence, Accountability, and the Limits of Delegated Judgment.Medical sciences (Basel, Switzerland) · 2026Review
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- LungSurg: A Generative AI System for Segmentation and Phase Classification in Thoracoscopic Lobectomy.MedComm · 2026Article
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Authors and funding
21 authors.
Funding
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Abstract
The integration of artificial intelligence (AI) into surgical practices is advancing towards greater intelligence and precision. This study assesses the potential of AI in video-assisted thoracoscopic surgery (VATS) lobectomy for lung cancer by developing an AI system named LungSurg. LungSurg comprises two interconnected networks: a segmentation network for identifying intrathoracic anatomy and surgical instruments, and a classification network for recognizing surgical phases. We prospectively collected 222 VATS lobectomy videos from eight centers, generating over 32,000 annotations and more than one million frames with phase information. In external validation, the segmentation network achieved mean Average precision scores of 0.745 for the left lung and 0.726 for the right lung across various instruments and anatomical structures. The classification network demonstrated Top-1 and Top-3 accuracies of 71.5% and 88.0%, respectively, in identifying 14 surgical phases. Comparative experiments revealed that LungSurg performed comparably to senior surgeons in anatomical identification and surpassed them in sensitivity. In addition, an educational study showed that surgical residents trained with LungSurg significantly improved their anatomical identification and phase classification skills compared to those using conventional methods. These results indicate that LungSurg accurately analyzes VATS lobectomy procedures, highlighting the feasibility and potential of AI-driven tools in enhancing thoracic surgical practices.
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Registered trials
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