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Artificial intelligence-assisted surgical simulation system based on non-enhanced computed tomography images in
Lei Wang1,2, Jing Hu3, Jianwei Gao4
1Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, China.
Journal of Thoracic Disease
|September 15, 2025
Summary
An artificial intelligence (AI)-assisted surgical simulation system improves thoracoscopic pulmonary segmentectomy by enhancing preoperative planning and intraoperative navigation. This AI system leads to reduced operative times, less blood loss, and fewer complications for better patient recovery.
Area of Science:
- Thoracic surgery
- Artificial intelligence in medicine
- Surgical simulation
Background:
- Traditional preoperative planning for thoracoscopic pulmonary segmentectomies using computed tomography (CT) images is time-consuming, labor-intensive, and lacks precision.
- These limitations can negatively impact surgical outcomes and patient recovery.
- An innovative AI-assisted surgical simulation system has been developed to address these challenges.
Purpose of the Study:
- To develop and evaluate an AI-assisted surgical simulation system for enhancing preoperative planning and intraoperative navigation in thoracoscopic pulmonary segmentectomies.
- To compare the clinical value of the AI system against traditional methods across all surgical phases.
Main Methods:
- The AI system (LungDimensionGo V1.0) utilizes EfficientDet for lung nodule detection and Mamba-Unet with SegRefiner for 3D lung model reconstruction from CT images.
- A comparative study included retrospective (n=125) and prospective (n=38) patient cohorts undergoing segmentectomy.
- The AI-assisted approach was evaluated against traditional surgical planning methods.
Main Results:
- No significant differences were observed in patient or tumor characteristics, or postoperative pathology between the groups.
- The AI-assisted group demonstrated shorter model reconstruction times and higher accuracy of anatomical structures.
- Significant advantages included reduced operative times, less intraoperative blood loss, shorter chest tube durations, and fewer postoperative complications.
Conclusions:
- The AI-assisted surgical simulation system significantly outperforms traditional methods in preoperative preparation, intraoperative guidance, and postoperative patient recovery.
- This technology offers a more precise and efficient approach to pulmonary segmentectomy.
- The findings support the adoption of AI in surgical planning for improved patient outcomes.
