评估InferVision的计算机辅助检测 (CAD) 算法用于结核病 (TB) 查,卢萨卡,赞比亚
Paul Somwe1, Minyoi Maimbolwa1, Kanema Chiyenu1
1Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.
PLOS global public health
|June 18, 2025
概括
在高负荷环境中,InferRead DR胸部显示了可接受的结核病 (TB) 查性能. 建议对这款人工智能工具进行进一步培训,以提高结核病检测准确度.
科学领域:
- 放射学和成像学 放射学和成像学
- 传染病诊断 传染病诊断 传染病诊断
- 医疗保健中的人工智能
背景情况:
- 结核病 (TB) 仍然是一个重大的全球卫生挑战,特别是在艾滋病毒负担较高的地区.
- 准确高效的结核病查工具对于早期检测和治疗至关重要.
- 人工智能 (AI) 提供了改善结核病查中的胸部X射线解释的潜力.
研究的目的:
- 评估InferRead DR Chest AI工具用于结核病查的诊断性能.
- 为了评估InferRead DR Chest在赞比亚卢萨卡高艾滋病毒和结核病发病率环境中的准确性.
- 确定人工智能工具是否符合世界卫生组织 (WHO) 针对结核病查的目标产品配置文件.
主要方法:
- 追溯分析了1890张匿名的15岁以上个人的胸部X射线图像.
- 将InferRead DR胸部分类与复合参考标准 (Xpert MTB/RIF或MTB文化) 的比较.
- 使用接收器操作特征曲线 (AUC) 下的面积和在定义的门上的灵敏度/特异性来评估性能.
主要成果:
- 在InferRead DR胸部的整体AUC为0.81.
- 前有结核病史的个体的AUC为0.71,HIV阳性个体的AUC为0.77.
- 在90.3%的灵敏度下,人工智能工具实现了39.2%的特异性,结核病得分切线为0.12.
结论:
- 在研究的高负担人群中,InferRead DR Chest显示了可接受的结核病查诊断性能.
- 人工智能工具的性能,特别是特异性,可能需要针对这个人口群体进行优化.
- 建议包括进一步培训和试点InferRead DR Chest在类似的环境中,以提高其实用性.
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