Application of artificial intelligence in pulmonary nodule analysis and lung segment resection planning for standardized training in thoracic surgery_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

GAO Chao , ZHOU Xiaoyun , GUO Chao , LIU Hongsheng , LI Shanqing ,  LIANG Naixin

Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, P. R. China;

Corresponding author：

LIANG Naixin, Email: pumchnelson@163.com

Keywords：

Artificial intelligence; thoracic surgery; pulmonary nodules; pulmonary segment anatomy; teaching

DOI：

10.7507/1007-4848.202410091

Video：

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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.

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1. 王辰, 齐学进, 陈昕煜, 等. 我国住院医师规范化培训制度的正式建立与政策体系. 中华医学杂志, 2015, 95(14): 1041-1043.Wang C, Qi XJ, Chen XY, et al. Formal establishment and policy system of standardized residency training in China. Natl Med J China, 2015, 95(14): 1041-1043.
2. Zhu J, Li W, Chen L. Doctors in China: Improving quality through modernization of residency education. Lancet, 2016, 388(10054): 1922-1929.
3. Dobey BC. Educating medical providers in the era of artificial intelligence. J Physician Assist Educ, 2023, 34(2): 168-170.
4. China Consortium of Elite Teaching Hospitals. Establishment of the resident core competency milestone evaluation system in China consortium of elite teaching hospitals. Med J Pek Union Med Coll Hosp, 2023, 14: 978-982.
5. Wang S, Yang L, Li M, et al. Medical education and artificial intelligence: Web of Science-based bibliometric analysis (2013-2022). JMIR Med Educ, 2024, 10: e51411.
6. Kolachalama VB, Garg PS. Machine learning and medical education. NPJ Digit Med, 2018, 1: 54.
7. Wenhang S, Shenting Z, Shaohua C. Prospects for the development of medical professionalism education in the AI perspective: A qualitative study of Chinese postgraduate medical students' written reflections. Med Teach, 2025. [Epub ahead of print].
8. Jiang H, Vimalesvaran S, Wang JK, et al. Virtual reality in medical students' education: Scoping review. JMIR Med Educ, 2022, 8(1): e34860.
9. Zheng Z, Ren M, Li B, et al. Application value of artificial intelligence-assisted three-dimensional reconstruction in planning thoracoscopic segmentectomy. Chin J Lung Cancer, 2023, 26(7): 515-522.
10. You J, Zhang G, Gao X, et al. Value of PET/CT combined with CT three-dimensional reconstruction in distinguishing different pathological subtypes of early lung adenocarcinoma. Chin J Lung Cancer, 2021, 24(7): 468-474.
11. Zhang Y, Jiang B, Zhang L, et al. Lung nodule detectability of artificial intelligence-assisted CT image reading in lung cancer screening. Curr Med Imaging, 2022, 18(3): 327-334.
12. Xue J, Xing F, Liu Y, et al. A 3D digital model for the diagnosis and treatment of pulmonary nodules. J Vis Exp, 2023, 195: e65423.
13. Ziegelmayer S, Marka AW, Strenzke M, et al. Speed and efficiency: Evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging. Eur Radiol, 2024. [Epub ahead of print].
14. Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: Integrative review. JMIR Med Educ, 2019, 5(1): e13930.
15. Mir MM, Mir GM, Raina NT, et al. Application of artificial intelligence in medical education: Current scenario and future perspectives. J Adv Med Educ Prof, 2023, 11(3): 133-140.
16. Luan H, Geczy P, Lai H, et al. Challenges and future directions of big data and artificial intelligence in education. Front Psychol, 2020, 11: 580820.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesApplication of artificial intelligence in pulmonary nodule analysis and lung segment resection planning for standardized training in thoracic surgery

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