没有代码的机器学习:验证了用于分类关节骨骨折的用例方法
Giridhar Dasegowda1, James Yuichi Sato1, Daniel C Elton1
1Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Mass General Brigham AI, USA.
Clinical imaging
|June 5, 2024
概括
一个无代码的机器学习平台使医生能够构建用于分析关节骨折放射图的AI模型. 该工具在从多中心成像数据中识别骨折时实现了90%的灵敏度和87%的特异性.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 人工智能在医学中的应用
背景情况:
- 医生对机器学习 (ML) 工具的访问受到编程要求的限制.
- 开发用于医学图像分析的AI模型通常需要专门的专业知识.
- 需要用户友好的平台,使临床医生能够利用ML进行诊断任务.
研究的目的:
- 为医生创建和评估一个无代码机器学习 (NML) 平台.
- 使非编程医生能够开发和测试NML模型.
- 使用开发的NML平台对关节骨X射线图进行骨折存在的分类.
主要方法:
- 一项回顾性研究使用了来自13家医院的2039名患者的4135张关节骨X射线图.
- 通过网络访问医院档案,NML平台自动检索DICOM图像.
- 该平台训练了一个ML模型,提供性能指标,如灵敏度,特异性和AUC.
主要成果:
- 在NML平台上,成功获取了94.7%符合条件的X光照.
- 经过训练的ML模型在识别关节骨骨折时实现了90%的灵敏度,87%的特异性和88%的准确性.
- 该模型表现出高诊断性能,AUC为0.95.5.
结论:
- 一个无代码的机器学习平台是可行的,医生可以创建诊断模型.
- 该NML平台促进了使用多中心成像数据集开发和测试ML模型.
- 这项技术可以使临床医生能够利用人工智能进行放射学分类,例如检测关节骨骨折.
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