医学教育管理

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AI辅助3D重建可视化教学模式在胸部CT解剖教学中的构建与评价

  

  1. 首都医科大学附属北京世纪坛医院医学影像科,北京 100038
  • 收稿日期:2025-12-01 修回日期:2026-01-23 出版日期:2026-07-02 发布日期:2026-07-02

Construction and evaluation of an AI-Assisted 3D reconstruction visualization teaching model in chest CT anatomy teaching

  1. Department of Medical Imaging, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
  • Received:2025-12-01 Revised:2026-01-23 Online:2026-07-02 Published:2026-07-02

摘要: 目的 探讨基于3D 重建的人工智能(artificial intelligence, AI)影像学可视化教学法在胸部电子计算机断层扫描(computed tomography, CT)解剖教学中的应用价值,解决传统教学中胸部解剖结构复杂、空间想象要求高、阅片难度大的难点。方法 选取2023年1月—2024年12月在首都医科大学附属北京世纪坛医院参与胸部CT解剖教学的100名住院医师作为研究对象,采用随机数字表法分为对照组(n=50)和试验组(n=50)。对照组学生采用传统教学法,以解剖图谱、器官模型结合二维CT图像授课,试验组学生采用 AI辅助3D重建可视化教学法,通过全自动三维重建软件将胸部CT图像转化为立体模型,结合以问题为基础的教学法(problem-based learning, PBL)开展交互式教学。教学结束后,通过理论考试、实践技能考核及满意度问卷评估教学效果。结果 试验组学生的理论考试成绩[(87.23±4.35)分]高于对照组[(81.05±5.21)分](P<0.001);在实践技能考核中,试验组学生CT影像阅读[(17.86±1.52)分]、病灶定位[(18.12±1.37)分]、临床思维[(17.69±1.45)分]及术中评分[(18.35±1.28)分],均优于对照组对应指标[(14.23±1.86)分、(13.95±1.74)分、(14.51±1.63)分、(14.87±1.59)分](P<0.001)。试验组学生总体教学满意度[(79.02±5.58)分]高于对照组[(70.75±6.09)分](P<0.001),92.00%的学生认为该方法能提升学习兴趣。结论 基于3D重建的AI影像学可视化教学法可直观呈现胸部复杂解剖结构,显著提高学生的理论水平、实践技能及学习积极性,为胸部CT解剖教学提供有效新模式。


关键词:  , 3D重建;人工智能;影像学可视化;胸部 CT;解剖教学;教学效果

Abstract: Objective To explore the application value of artificial intelligence (AI) imaging visualization teaching method based on 3D reconstruction in chest CT anatomy teaching, and solve the pain points of complex chest anatomical structure, high requirement for spatial imagination, and difficulty in image reading in traditional teaching.Methods A total of 100 resident physicians who participated in chest CT anatomy teaching in Beijing Shijitan Hospital, Capital Medical University, from January 2023 to December 2024 were enrolled as subjects and they were randomly divided into the experimental group (n=50) and the control group (n=50) using the random number table method. The control group adopted the traditional teaching method, lectures combining with anatomical atlases, organ models and two-dimensional CT images. The experimental group adopted the AI-assisted 3D reconstruction visualization teaching method, which converted chest CT images into three-dimensional models through automatic 3D reconstruction software and carried out interactive teaching combined with problem-based learning (PBL). After the teaching, the teaching effects were evaluated through theoretical examinations, practical skill assessments and satisfaction questionnaires.Results The theoretical examination scores of the experimental group [(87.23±4.35) points] were significantly higher than those of the control group [(81.05±5.21) points](P<0.001). In the practical skill assessment, the scores of CT image reading [(17.86±1.52) points], lesion localization [(18.12±1.37) points], clinical thinking [(17.69±1.45) points] and intraoperative score (18.35±1.28) in the experimental group were all superior to the corresponding indicators in the control group [(14.23±1.86) points, (13.95±1.74) points, (14.51±1.63) points, (14.87±1.59) points] (all P<0.001). The overall teaching satisfaction score of the experimental group [(79.02±5.58) points] was higher than that of the control group [(70.75±6.09) points] (P<0.001), and 92.00% of the students believed that this method could improve learning interest.Conclusion AI imaging visualization teaching method based on 3D reconstruction can intuitively present complex thoracic anatomical structures, significantly improve students' theoretical level, practical skills and learning enthusiasm, and provide an efficient new model for chest CT anatomy teaching.


Key words:  3D reconstruction, artificial intelligence, imaging visualization, chest CT, anatomy teaching, teaching effect