关心点的人工智能辅助步骤超声波肺胸部诊断诊断
Kyungsang Kim1, Fabiola Macruz2, Dufan Wu1
1Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States of America.
Physics in medicine and biology
|September 19, 2023
概括
这项研究引入了一种人工智能支持的框架,以帮助医疗保健提供者进行肺部胸腔诊断的紧急肺部超声波. 人工智能通过扫描指导用户,提高了对经验较少的临床医生的准确性和可访问性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 紧急医疗 紧急医疗
背景情况:
- 护理点超声波 (POCUS) 在紧急情况下是有价值的,但受临床医生的专业知识限制.
- 在紧急情况下,熟练的临床医生很少,这阻碍了POCUS的采用.
- 在紧急肺部超声波中帮助缺乏经验的提供者至关重要.
研究的目的:
- 开发一个人工智能辅助的框架,通过肺部超声波来诊断肺胸.
- 通过肺部超声波扫描的连续步骤指导医疗保健提供者.
- 为了提高紧急肺部超声波的可访问性和准确性.
主要方法:
- 一个框架被设计成模仿专家临床医生对肺部超声波的工作流程.
- 开发了两种深度学习 (DL) 模型:一种用于质量保证 (QA) 和肺线定位,另一种用于肺滑动分类.
- 一个EfficientNet-Lite0模型被优化为移动设备推断 (<300万参数).
主要成果:
- 质量保证和肺滑动分类模型实现了>95%的AUC.
- 感兴趣地区 (ROI) 的表现达到了89%的子相似系数.
- 人工智能框架对回顾性数据的模拟产生了89%的AUC.
结论:
- 人工智能框架为肺胸部诊断提供了一个可理解的,一步一步的指南.
- 该系统整合了质量保证和分类,以改善临床工作流程.
- 高精度和实时推断能力被证明用于紧急肺部超声波.
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