开发和评估一个自动化协议推系统的胸部CT使用自然语言处理与CLEVER术语词的替换术语
Patrik Rogalla1, Jennifer Fratesi1, Sonja Kandel1
1Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
概括
对于胸部CT成像要求的新协议推系统 (PRS) 的准确性与人类放射科医生相美. 这种人工智能工具可以帮助有效地管理日益增长的放射科医生工作负载.
科学领域:
- 放射学 放射学是指放射学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 胸部CT成像请求正在增加,这给放射科医生带来了工作负载挑战.
- 准确的成像请求协议对于有效的诊断和患者护理至关重要.
- 现有的协议化方法可能耗时且容易变化.
研究的目的:
- 评估一个自动化的协议推系统 (PRS) 对于胸部CT成像请求的临床性能.
- 将PRS的准确性和临床可接受性与人类专家协议进行比较.
- 确定PRS在协助放射科医生的工作负载方面的潜力.
主要方法:
- 一个多项逻辑回归分类器在历史的胸部CT成像请求上使用一个词袋模型进行训练.
- 通过四个人类读者编写的300个临床执行协议 (CEP) 来评估PRS.
- 使用Fleiss' Kappa评估了读者同意,并计算了PRS和CEP的准确性/可接受性得分.
主要成果:
- 该PRS的准确率为84.3%,临床可接受度为98.6%.
- 人类读者达到83.0%的准确率和99.3%的临床可接受性.
- 读者一致性 (Fleiss' Kappa) 高达0.805,表明了良好的读者之间的可靠性.
结论:
- 协议推系统 (PRS) 在进行胸部CT请求的协议时,与人类放射科医生相似的准确性.
- PRS显示出作为一个有价值的工具的潜力,帮助放射科医生管理不断增加的成像请求量.
- 像PRS这样的自动化系统可以支持临床决策,并提高放射科部门的效率.
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