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Artificial intelligence driven 3D reconstruction for enhanced lung surgery planning
Xiuyuan Chen1,2,3,4,5, Chenyang Dai6, Muyun Peng7
1Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
Nature Communications
|May 1, 2025
Summary
An AI-powered 3D reconstruction system enhances lung surgery planning by improving anatomical variant identification and reducing errors. This AI tool significantly decreases preoperative planning time, boosting surgeon confidence and efficiency.
Area of Science:
- Thoracic Surgery
- Medical Imaging
- Artificial Intelligence
Background:
- Complex lung surgeries require precise preoperative planning.
- Current 3D reconstruction methods face adoption barriers like time and validation.
- Enhanced imaging support is crucial for improving surgical outcomes.
Purpose of the Study:
- To evaluate an AI-driven 3D reconstruction system for pulmonary vessels and bronchi.
- To assess the system's impact on anatomical variant identification and preoperative planning efficiency.
- To determine the system's utility in thoracic surgery planning.
Main Methods:
- Retrospective, multi-center, multi-reader, multi-case study.
- Ten thoracic surgeons assessed 140 cases with and without AI system assistance.
- Two-stage crossover design evaluating system's impact on accuracy and time.
Main Results:
- AI system improved anatomical variant identification accuracy by 8% (p<0.01), reducing errors by 41%.
- Operation procedure selection accuracy improved by 8%, with a 35% decrease in errors.
- Preoperative planning time decreased by 25%; user satisfaction was 99%.
Conclusions:
- AI-driven 3D reconstruction significantly enhances anatomical variant identification in thoracic surgery.
- The system addresses critical needs in preoperative planning, improving accuracy and efficiency.
- Benefits were consistent across surgeons of varying experience levels.
