基于深度学习的反模型的开发,以帮助医学学生学习脏超声波采集:混合方法研究研究
Andy Cheuk Nam Hwang1, Rahul Singh1, Elizabeth Ann Barrett2
1Department of Diagnostic Radiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Rd, Pok Fu Lam, Hong Kong, China, +852 22553307.
JMIR medical education
|March 11, 2026
概括
深度学习模型为护理点超声波培训提供自动反,增强医学学生的自我调节学习和技能获取. 这种工具对改善超声波教育充满希望.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 护理中心超声波检查
背景情况:
- 在医疗课程中整合 Point-of-care超声波 (POCUS) 需要教师的专业知识来进行有效的培训.
- 越来越需要可扩展的解决方案来支持POCUS指令和反.
研究的目的:
- 开发和评估基于深度学习的反模型,作为POCUS培训的辅助工具.
- 评估人工智能反模型对医学学生学习经验和技能获取的影响.
主要方法:
- 在2807个脏超声波图像上训练了一种级联深度学习模型,用于自动化质量评估.
- 该模型将图像分为最佳,次优和不正确的类别,次优图像进一步分为子类别.
- 混合方法分析,包括重点小组和问卷,评估学生反和欧安组织的分数在实施前和后进行了比较.
主要成果:
- 深度学习模型鼓励自我调节的学习,尽管课程和硬件问题提出了挑战.
- 学生对模型的可用性和帮助度的满意度从32%到76%不等.
- 目标结构化临床检查 (OSCE) 平均得分在实施后呈现改善趋势 (9.73/10对比之前的9.35-9.45/10).
结论:
- 基于深度学习的反模型已成功开发并部署用于床边超声波培训.
- 人工智能工具促进了积极的学习参与,并增强了初学者医学学生的自我调节学习.
- 这项创新表明了改善医学教育中的超声波技能获取的潜力.
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