基于人工智能的急诊室肺炎处置预测工具的未来外部验证
Aaditeya Jhaveri1, Farbod Abolhassani2,3,4, Benjamin Fine1,2
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
概括
一个人工智能工具在使用胸部X射线用于社区获得的肺炎来预测患者情绪方面表现中等,一致. 人工智能工具在指导年轻患者出院和老年患者入院方面显示出潜在的实用性.
科学领域:
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
- 紧急医疗 紧急医疗
背景情况:
- 在急诊室 (ED) 诊断社区获得性肺炎 (CAP) 往往需要及时做出处置决定.
- 胸部X射线 (CXR) 对CAP评估至关重要,正在开发人工智能工具来帮助解释.
- 预测患者倾向 (例如,入院与出院) 可以优化ED工作流程和患者管理.
研究的目的:
- 评估外部人工智能工具在使用ED中的CXRs预测患者对CAP的倾向方面的表现.
- 为了比较人工智能工具的回顾性和前性外部验证结果.
- 评估各种患者子组的AI工具性能,以告知部署.
主要方法:
- 一个卷积神经网络 (CNN) 的人工智能工具在17,689名患者上进行了追溯验证,并在3,062名疑似CAP的患者上进行了前性验证.
- 计算了标准性能指标,包括曲线下的面积 (AUC),准确度,灵敏度,特异性,正预测值 (PPV) 和负预测值 (NPV).
- 根据年龄,性别,成像方式和CXR预测 (PA vs AP) 进行了子组分析.
主要成果:
- 人工智能模型在回顾性和前性验证中都实现了67%的一致AUC.
- 展望验证显示,敏感性 (65%) 和PPV (64%) 的增加不显著,特异性 (68%) 和NPV (69%) 略有下降.
- 在年轻患者中观察到高NPV (95%),在老年患者中观察到高PPV (81%);然而,在特定的女性子组中,表现有显著差异.
结论:
- 人工智能工具在回顾性和前性验证中显示出适度和一致的性能,用于预测CAP患者的处置.
- 该工具显示了在年轻患者中加速出院决策 (高NPV) 和在老年患者中告知入院决策 (高PPV) 的潜力.
- 需要进行进一步的研究,以确定各种临床环境中后期和前性验证方法的必要性.
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