与基于深度学习的成像模型并行设计COVID-19诊断的CORI得分
Telly Kamelia1,2, Benny Zulkarnaien3,4, Wita Septiyanti3,4
1Division of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia.
Narra J
|September 15, 2025
概括
一个使用胸部X射线和临床数据的人工智能 (AI) 模型显示,它对诊断COVID-19非常有希望,特别是在RT-PCR测试有限的地方. 这种人工智能辅助工具在识别2019年新冠肺炎病例方面取得了很高的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 随着COVID-19大流行,全球医疗保健受到压力,突显了对RT-PCR可访问的诊断替代品的需求,特别是在资源有限的环境中.
- 胸部X射线与人工智能相结合,为快速和广泛的COVID-19检测提供了一个潜在的解决方案.
研究的目的:
- 开发一个人工智能辅助的诊断模型,整合胸部X射线图像和临床数据.
- 使用深度学习的ResNet架构创建一个COVID-19风险指数 (CORI) 评分.
- 评估AI模型和CORI评分的诊断性能.
主要方法:
- 印度尼西亚雅加达的一项多中心队列研究,涉及367名参与者 (COVID-19阳性,非COVID-19肺炎,健康对照).
- 收集胸部X射线图像,临床参数 (发烧,咳,氧和度) 和实验室发现 (D-二分体,C-反应蛋白).
- 使用集成数据开发和验证基于ResNet的深度学习模型和CORI评分.
主要成果:
- 在内部验证中,ResNet模型实现了91%的准确性,94%的灵敏性和92%的特异性,并在外部验证中确定了82%的COVID-19病例.
- 综合方法 (成像,临床,实验室数据) 的AUC为0.98,灵敏度>95%.
- 科里评分显示了高的诊断性能:96.6%的准确性,98%的灵敏性,95.4%的特异性,99.5%的NPV和91.1%的PPV.
结论:
- 基于ResNet的AI模型和CORI评分显示出作为COVID-19诊断工具的巨大潜力.
- 人工智能模型的性能相当于印度尼西亚经验丰富的胸部放射科医生.
- 这些人工智能驱动的工具可以改善COVID-19的诊断,特别是在资源有限的环境中.
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