基于深度学习的软件在急诊室进行胸部X射线分析的有效性
Sathiyamurthy Selvam1, Olivier Peyrony2, Arben Elezi3
1Department of Radiology, AP-HP, Hôpital Saint-Louis, 75010 Paris, France.
Diagnostic and interventional imaging
|April 3, 2025
概括
深度学习 (DL) 计算机辅助检测 (CAD) 显著改善了在急诊室检测关键胸部X射线异常的情况. 这种人工智能工具提高了放射科医生对巩固和结节等疾病的敏感性,有助于更快的诊断.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 紧急医疗 紧急医疗
背景情况:
- 胸部X射线在急诊室对于诊断呼吸系统疾病至关重要.
- 计算机辅助检测 (CAD) 系统旨在提高诊断准确度.
- 深度学习 (DL) 为医学图像分析提供先进的模式识别功能.
研究的目的:
- 评估基于DL的CAD系统在检测急诊室胸部X射线上的异常方面的有效性.
- 在临床信息可用时,评估DL-CAD对阅读器性能的影响.
主要方法:
- 追溯分析404个急诊室患者的胸部X射线,这些患者有呼吸系统症状.
- 五位读者 (放射科医生和急诊医生) 两次解读了X射线:一次是无助的,一次是有DL-CAD协助的.
- 两次阅读会之间的读者表现和观察者之间的协议 (Fleiss' kappa) 的比较.
主要成果:
- DL-CAD改善了对巩固 (8.3%),多叶膜溢出 (7.9%) 和结节 (29.5%) 的敏感性.
- 特异性仍然很高 (>89%),结节和中骨/骨质量略有下降.
- 观察者间的一致性随着DL-CAD的增加而增加,所有评估异常的kappa值得到改善.
结论:
- 在急诊室,DL辅助阅读增强了对严重的胸部X射线异常的敏感性.
- 即使临床医生可以获得患者的临床信息,DL-CAD的好处也很明显.
- DL-CAD显示出作为一种有价值的工具来提高紧急放射学诊断准确性的承诺.
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