Department of Medical Imaging, Second Affiliated Hospital of Qiqihar Medical University, 37 West Zhonghua Road, Qiqihar, Heilongjiang, 161006, China.
Center for Higher Education Research and Teaching Quality Evaluation, Harbin Medical University, Harbin, Heilongjiang, 150000, China.
BMC Med Educ. 2024 Apr 11;24(1):405. doi: 10.1186/s12909-024-05382-6.
In medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) technique is gradually entering radiology. It converts two-dimensional images into three-dimensional images, and AI can assist in image diagnosis. However, the application of artificial intelligence in medical education is still in its early stages. The purpose of this study is to explore the application value of AISD software based on VDR technique in medical imaging practical teaching, and to provide a basis for improving medical imaging practical teaching.
Totally 41 students majoring in clinical medicine in 2017 were enrolled as the experiment group. AISD software based on VDR was used in practical teaching of medical imaging to display 3D images and mark lesions with AISD. Then annotations were provided and diagnostic suggestions were given. Also 43 students majoring in clinical medicine from 2016 were chosen as the control group, who were taught with the conventional film and multimedia teaching methods. The exam results and evaluation scales were compared statistically between groups.
The total skill scores of the test group were significantly higher compared with the control group (84.51 ± 3.81 vs. 80.67 ± 5.43). The scores of computed tomography (CT) diagnosis (49.93 ± 3.59 vs. 46.60 ± 4.89) and magnetic resonance (MR) diagnosis (17.41 ± 1.00 vs. 16.93 ± 1.14) of the experiment group were both significantly higher. The scores of academic self-efficacy (82.17 ± 4.67) and self-directed learning ability (235.56 ± 13.50) of the group were significantly higher compared with the control group (78.93 ± 6.29, 226.35 ± 13.90).
Applying AISD software based on VDR to medical imaging practice teaching can enable students to timely obtain AI annotated lesion information and 3D images, which may help improve their image reading skills and enhance their academic self-efficacy and self-directed learning abilities.
在医学影像学课程中，由于解剖关系复杂、实践课程学时和教师数量有限，如何提高实践技能教学质量和自主学习能力一直是高等医学教育面临的挑战。基于容积数据重建（VDR）技术的人工智能辅助诊断（AISD）软件逐渐进入放射学领域。它将二维图像转换为三维图像，人工智能可以辅助图像诊断。然而，人工智能在医学教育中的应用仍处于早期阶段。本研究旨在探讨基于 VDR 技术的 AISD 软件在医学影像学实践教学中的应用价值，为提高医学影像学实践教学水平提供依据。
选取 2017 年临床医学专业 41 名学生为实验组，在医学影像学实践教学中应用基于 VDR 的 AISD 软件显示三维图像，并对 AISD 标记的病变进行标注，给出诊断建议。同时选取 2016 年临床医学专业 43 名学生为对照组，采用常规胶片和多媒体教学方法进行教学。对两组学生的考试成绩和评价量表进行统计学比较。
实验组学生的总技能评分明显高于对照组（84.51±3.81 vs. 80.67±5.43）。实验组 CT 诊断（49.93±3.59 vs. 46.60±4.89）和磁共振（MR）诊断（17.41±1.00 vs. 16.93±1.14）的评分均明显高于对照组。实验组的学业自我效能感（82.17±4.67）和自主学习能力（235.56±13.50）评分明显高于对照组（78.93±6.29，226.35±13.90）。
将基于 VDR 的 AISD 软件应用于医学影像学实践教学，可使学生及时获得人工智能标注的病变信息和三维图像，有助于提高学生的图像阅读能力，增强学生的学业自我效能感和自主学习能力。
