对于儿科胸部X射线诊断的深度学习:重新利用为成年人开发的商业工具
Prerana Agarwal1, Alexander Rau1, Helen Ngo1
1Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Freiburg, Germany.
PloS one
|July 24, 2025
概括
将成人人工智能 (AI) 工具重新用于儿科胸部X射线显示出高诊断性能. 这种方法可以支持临床工作流程,直到开发专门的儿科人工智能解决方案.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 儿科成像学 儿科成像学
背景情况:
- 放射学工作流程的商业人工智能 (AI) 工具正在增加.
- 专门用于儿科成像的AI解决方案在很大程度上是不可用的.
研究的目的:
- 在儿科临床数据集中调查针对成人的人工智能工具 (Lunit INSIGHT CXR) 的诊断性能.
- 评估将成人AI工具重新用于儿科胸部X射线图解读的可行性.
主要方法:
- 使用AI工具分析了958张儿科胸部X射线图 (年龄为2-14岁).
- 由董事会认证的放射科医生制定的参考标准,对不一致的发现达成共识.
- 用曲线下的面积 (AUC),灵敏度和特异性与成人和优化值验证的诊断性能.
主要成果:
- 人工智能工具使用成人值显示出高性能 (AUC 0.94),20.9%的X光照显示出相关的病理.
- 在年龄较大的儿童中,表现显著更高 (AUC 0.96),并且在优化的儿科门下得到改善.
- 在优化值的老年儿童中,敏感性和特异性分别达到87.5%和82.3%.
结论:
- 重新利用成人人工智能工具是支持儿科放射学工作流程的可行策略.
- 这种方法可以弥合差距,直到专门的儿科人工智能解决方案变得可用.
- 该研究强调了适应现有AI技术的潜力,以适应不同患者群体的需求.
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