LungSurg: A Generative AI System for Segmentation and Phase Classification in Thoracoscopic Lobectomy
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) enhances thoracic surgery. The LungSurg system accurately analyzes video-assisted thoracoscopic surgery (VATS) lobectomy procedures, improving anatomical identification and surgical phase recognition for better training and practice.
Area Of Science
- Thoracic Surgery
- Artificial Intelligence
- Medical Imaging
Background
- Surgical practices are increasingly integrating artificial intelligence (AI) for enhanced precision.
- Video-assisted thoracoscopic surgery (VATS) lobectomy for lung cancer presents opportunities for AI-driven advancements.
Purpose Of The Study
- To assess the potential of AI in VATS lobectomy by developing and evaluating an AI system named LungSurg.
- LungSurg aims to improve the analysis of intrathoracic anatomy, surgical instruments, and surgical phases during VATS lobectomy.
Main Methods
- Development of LungSurg, featuring interconnected segmentation and classification networks.
- Prospective collection of 222 VATS lobectomy videos from eight centers, with extensive annotations and frame-level phase information.
- External validation of segmentation and classification network performance, alongside comparative studies with senior surgeons and an educational assessment with surgical residents.
Main Results
- The segmentation network achieved high mean Average precision scores for lung and instrument identification (0.745 for left lung, 0.726 for right lung).
- The classification network demonstrated strong Top-1 (71.5%) and Top-3 (88.0%) accuracies in identifying 14 distinct surgical phases.
- LungSurg showed comparable anatomical identification to senior surgeons and superior sensitivity, with significant skill improvement in residents trained using the system.
Conclusions
- LungSurg accurately analyzes VATS lobectomy procedures, demonstrating the feasibility of AI tools in thoracic surgery.
- AI-driven systems like LungSurg have the potential to enhance surgical precision, training, and overall thoracic surgical practices.
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