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扩大结核病的分子诊断覆盖范围,结合计算机辅助的胸部X光学和唾液样本组合:来自四个高负担国家的建模研究
Andrew James Codlin1,2, Luan Nguyen Quang Vo1,2, Tushar Garg3
1Friends for International TB Relief, Hanoi, Viet Nam.
BMC global and public health
|August 5, 2024
概括
结合用于胸部X射线 (CXR) 解释的人工智能 (AI) 与唾液样本组合,可显著降低结核病 (TB) 诊断成本. 这一战略可以扩大全球更多患者获得基本分子诊断测试的机会.
科学领域:
- 传染性疾病 传染性疾病
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
背景情况:
- 由于成本高昂,对结核病 (TB) 的分子诊断测试的限制影响全球不到一半的受影响人群.
- 对于胸部X射线 (CXR) 解释和唾液样本聚合的人工智能 (AI) 是结核病检测的成本降低策略.
- 将人工智能辅助的CXR解释与聚合测试相结合,有可能大大节省成本,并扩大对分子诊断的访问.
研究的目的:
- 模拟人工智能辅助的CXR解释和唾液样本聚合对结核病分子诊断测试的成本和可访问性的综合影响.
- 估计消耗品的潜在节约,并评估诊断覆盖范围的理论扩展.
- 利用人工智能确定最佳的查和测试策略,以指导分子测试决策.
主要方法:
- 利用来自孟加拉国,尼日利亚,越南和赞比亚的活跃病例发现的Xpert测试和阳性数据,按结核病的AI概率得分进行细分.
- 建模了四种查和测试方法,包括人工智能辅助的CXR解释,用于个人和聚合测试.
- 计算的累积节省和诊断覆盖范围的理论扩展,与通用Xpert测试的基线相比,保持5%的虚假负率.
主要成果:
- 最优的方法是使用人工智能排除在低概率分数中进行测试,并指导在中等/高概率分数中进行聚合/个人测试.
- 在Xpert测试中,累计的节省从赞比亚的50.8%到孟加拉国和越南的61.5%不等.
- 从理论上讲,诊断覆盖范围可以在不同的方法和国家之间以34%至160%的速度扩展,这取决于所产生的节省.
结论:
- 整合人工智能驱动的CXR解释与聚合测试优化了结核病诊断测试的使用,可能扩大了分子测试的访问.
- 最佳的人工智能门和聚合测试策略因国家而异,表明需要针对不同人群和环境量身定制的方法.
- 这种联合战略提供了一个可行的解决方案,以提高全球获得关键结核病诊断工具的机会.
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