加速基于咳的算法用于肺结核查:CODA TB DREAM挑战的结果
Devan Jaganath1,2, Solveig K Sieberts3, Mihaja Raberahona4,5
1Division of Pediatric Infectious Diseases, University of California, San Francisco, California, San Francisco, USA.
Open forum infectious diseases
|October 9, 2025
概括
开放获取数据挑战了使用咳声音进行结核病查的快速先进的人工智能. 结合咳和临床数据显著提高了诊断准确度,显示了新的查工具的前景.
科学领域:
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
- 公共卫生 公共卫生
背景情况:
- 开放获取数据的挑战加速了人工智能驱动的健康工具的创新.
- 结核病咳诊断算法 (CODA TB) DREAM挑战专注于开发用于结核病查的AI.
- 咳声音分析为检测结核病提供了一种新的方法.
研究的目的:
- 开发和验证使用咳声音进行结核病查的人工智能算法.
- 为了比较使用咳声音单独与咳声音与临床数据相结合的模型的性能.
- 评估快速,开放的数据访问挑战的可行性,以推进诊断工具.
主要方法:
- 收集了来自7个国家的2,143名成年人持续咳的数据.
- 参与者通过智能手机提供咳记录,并接受标准结核病评估.
- 机器学习模型是使用训练数据开发的,并通过AUROC和pAUROC评估的独立测试集进行验证.
主要成果:
- 提交了11个仅咳和6个咳加临床模型.
- 只有咳的模型达到0.74的最大AUROC,在80%的灵敏度下具有55.5%的特异性.
- 纳入临床数据改善了AUROC (0.78-0.83),其中5/6个模型达到目标pAUROC,在80%的灵敏度下达到73.8%的特异性.
结论:
- 科达结核病梦想挑战赛迅速促进了基于人工智能的结核病查算法的开发.
- 将咳声音与临床数据相结合,显著提高了诊断性能.
- 这些人工智能工具显示出作为非侵入性结核病查方法的巨大潜力.
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