使用人工智能进行创伤检查的专注腹部超声波质量评估工具
John Cull1, Dustin Morrow, Caleb Manasco
1From the Department of Surgery (J.C., A.V., T.C.), and Emergency Department (D.M., C.M., J.E.), Prisma Health Upstate, Greenville, South Carolina; and Holcombe Department of Electrical and Computer Engineering (H.S.), Clemson University, Clemson, South Carolina.
The journal of trauma and acute care surgery
|September 27, 2024
概括
现在,人工智能工具可以评估创伤焦点腹部超声波 (FAST) 检查的质量,大大减少审查员需要检查的剪辑数量. 这种人工智能 (AI) 系统提高了评估FAST图像的准确性和效率.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 紧急医疗 诊断 诊断 诊断 诊断
背景情况:
- 目前用于评估创伤焦点腹部超声波 (FAST) 图像质量的方法缺乏明确的标准.
- 现有的工具往往评估超声波仪的技能,而不是检查质量.
- 需要一个标准化,可靠的系统来进行FAST图像质量评估.
研究的目的:
- 开发FAST考试的分级系统,并达成高度的编码器间协议.
- 通过使用人工智能 (AI) 为FAST考试创建一个自动化评估工具.
主要方法:
- 五位编码人员为FAST剪辑分配了质量评分 (1-5),其中10%的可靠性被三次审查.
- 一个人工智能模型被训练使用一个数据集的1514片段,分为训练,验证和测试集.
- 人工智能模型是为了预测FAST检查质量得分而开发的,它区分了通过 (得分≥3) 和失败的剪辑.
主要成果:
- 最终的数据集在通过/失败预测上实现了编码者之间94%的协议,Krippendorff α为66%.
- 人工智能模型显示,与无助审查者相比,需要手动审查的剪辑减少了五倍.
- 人工智能模型正确识别了85%的传递片段,同时显著降低了审查负担.
结论:
- 人工智能集成显示了提高FAST图像评估准确性的巨大潜力.
- 开发的AI工具可以大大减轻专家医生审查FAST检查的工作量.
- 这种方法为更高效,更可靠的紧急超声波质量控制铺平了道路.
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