Application of artificial intelligence in pulmonary nodule analysis and lung segment resection planning for standardized training in thoracic surgery
- VernacularTitle:人工智能在胸外科规范化培训教学中肺结节分析与肺段切除规划的应用效果研究
- Author:
Chao GAO
1
;
Xiaoyun ZHOU
1
;
Chao GUO
1
;
Hongsheng LIU
1
;
Shanqing LI
1
;
Naixin LIANG
1
Author Information
1. Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, P. R. China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
thoracic surgery;
pulmonary nodules;
pulmonary segment anatomy;
teaching
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(04):469-472
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the application of artificial intelligence (AI) in the standardized training of thoracic surgery residents, specifically in enhancing clinical skills and anatomical understanding through AI-assisted lung nodule identification and lung segment anatomy teaching. Methods Thoracic surgery residents undergoing standardized training at Peking Union Medical College Hospital from September 2023 to September 2024 were selected. They were randomly assigned to a trial group and a control group using a random number table. The trial group used AI-assisted three-dimensional reconstruction technology for lung nodule identification, while the control group used conventional chest CT images. After basic teaching and self-practice, the ability to identify lung nodules on the same patient CT images was evaluated, and feedback was collected through questionnaires. Results A total of 72 residents participated in the study, including 30 (41.7%) males and 42 (58.3%) females, with an average age of (24.0±3.0) years. The trial group showed significantly better overall diagnostic accuracy for lung nodules (91.9% vs. 73.3%) and lung segment identification (100.0% vs. 83.70%) compared to the control group, and the reading time was significantly shorter [ (118.5±10.5) s vs. (332.1±20.2) s, P<0.01]. Questionnaire results indicated that 94.4% of the residents had a positive attitude toward AI technology, and 91.7% believed that it improved diagnostic accuracy. Conclusion AI-assisted teaching significantly improves thoracic surgery residents’ ability to read images and clinical thinking, providing a new direction for the reform of standardized training.
