胸部X射线人工智能选系统的外部验证:一个回顾性的独立临床研究
André Coutinho Castilla1, Iago de Paiva D'Amorim2, Maria Fernanda Barbosa Wanderley1
1NeuralMed A2 Tecnologia Ltda., Rua Pe João Manoel 1212, Cj 41, São Paulo 01411-010, SP, Brazil.
Diagnostics (Basel, Switzerland)
|November 27, 2025
概括
一个用于胸部X光学 (CXR) 的人工智能 (AI) 选系统在识别异常发现方面表现出强大的准确性. 这种人工智能工具显示了减少报告延迟和改善紧急情况下患者护理的潜力.
科学领域:
- 医学成像和放射学医学成像和放射学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 胸部放射学 (CXR) 是一种高容量诊断成像模式,由于放射科医生短缺而面临报告积压.
- 人工智能 (AI) 预选系统通过优先考虑紧急案件提供了一个潜在的解决方案.
- 商业AI算法的外部验证对于临床采用至关重要.
研究的目的:
- 为了外部验证TRIA AI驱动的CXR分类算法.
- 评估算法在区分正常与异常CXR研究中的性能.
- 评估AI在改善紧急护理工作流程方面的潜力.
主要方法:
- TRIA使用了两阶段的深度学习模型:图像细分和病理分类.
- 该系统在超过275,000个CXR上进行了训练,并在1045个外部案例上进行了验证.
- 使用大型语言模型 (LLM) 建立基准真相,以提取报告,并得到放射科医生的确认.
主要成果:
- 一般异常分类器的AUROC达到0.911.
- 特定的病理学模型表现出高性能 (例如,心壮病AUROC 0.968).
- 一个加权的整体策略产生了平衡的性能:准确度为0.854,灵敏度为0.845,特异性为0.861,AUROC为0.927.
结论:
- 在CXR解释方面,TRIA AI算法表现出强大而平衡的准确性.
- 临床整合TRIA可以减少报告延迟,提高患者安全.
- 建议进行进一步的前性多中心验证,以确认临床效用.
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