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测试人工智能模型的性能,充分性和适用性,用于儿童肺炎诊断

Sara Domínguez-Rodríguez¹, Helena Liz-López², Angel Panizo-LLedot²

¹Pediatric Research and Clinical Trials Unit (UPIC). Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Fundación para la Investigación Biomédica del Hospital 12 de Octubre, Madrid, Spain.

Computer methods and programs in biomedicine

|September 13, 2023

概括

这项研究引入了一个深度学习模型的试点测试,用于在儿科X射线中识别肺炎,使用贝叶斯潜伏类模型和可解释的AI来改善验证和医生接受.

科学领域:

医疗成像中的人工智能
儿科放射学儿科放射学
深度学习用于诊断.

背景情况:

社区获得性肺炎 (CAP) 是一个重要的儿童疾病.
目前对X射线解释深度学习模型的验证存在局限性.
一个完美的基本真理假设是不现实的,因为观察者的变化.

研究的目的:

试点测试一个深度卷积神经网络 (CNN) 模型,用于识别儿科胸部X射线 (CXRs) 中的整合.
通过纳入贝叶斯潜伏类模型 (BLCA) 和可解释AI (XAI) 来扩展标准验证工作流.
评估模型的准确性,适用性和医生在临床决策中的接受度.

主要方法:

使用190个儿科CXR进行的试点测试评估了基于CNN的支持决策工具 (SDT).
使用贝叶斯隐藏类模型 (BLCA) 估计了模型性能.
可解释人工智能 (XAI) 技术评估了SDT与高级医生和医疗住院医生的充分性和适用性.

主要成果:

美国有线电视新闻网 (CNN) 模型的传感度平均为90.9%,特异性为77.7%.
高级医生报告说,70%的人同意SDT对整合模式的识别.
与专家咨询相比,住院医生与SDT达成更好的协议,仅仅是专家咨询 (0.75对0.66卡帕指数).

结论:

试点测试表明,深度学习模型可能被低估了完美的地面真理假设.
该研究证实了该模型在儿科CXR中识别逻辑整合模式的能力.
自动化预读助手可以加速临床工作流程,提高诊断准确度.

关键词:

美国有线电视新闻网 (CNN)胸部X射线胸部X射线深度学习是一种深度学习.肺炎是一种肺炎.

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