论文标题
是否可以使用人工智能（AI）从胸部X射线中准确检测结核病（TB）？在高结核负担设置中对五种用于结核病筛查和分类的AI产品的评估
Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting
论文作者
论文摘要
可以训练人工智能（AI）产品以识别胸部X光片的结核病（TB）相关异常。各种AI产品在商业上可用，但是缺乏证据表明其彼此和放射科医生的性能如何。我们使用尚未用于培训任何商业AI产品的大数据集评估了五种AI软件产品，用于筛选和分类结核病。孟加拉国达卡的三个结核病筛查中心的个人（> = 15岁）被连续招募。所有CXR均由三名孟加拉国注册放射科医生和五个商业AI产品组成的组独立阅读：CAD4TB（V7），CheleReaddr（V2），Lunit Insight CXR（v4.9.0），JF CXR-1（v2）（V2）和QXR（V3）。所有五种AI产品都大大优于孟加拉国放射科医生。接收器操作特征曲线下的区域为QXR：90.81％（95％CI：90.33-91.29％），CAD4TB：90.34％（95％CI：89.81-90.87），Lunit Insight CXR CXR：88.61％（88.61％） （95％CI：84.27-85.54％）和JF CXR-1：84.89％（95％CI：84.26-85.53％）。只有QXR以90％的灵敏度为74.3％的特异性符合TPP。五种AI算法可以将所需的XPERT测试数量减少50％，同时保持敏感性以上90％以上。所有AI算法在年龄较大的人群和先前有结核病历史的人中的表现都较差。 AI产品可以是高负担区域和表现较高的人类读者的高度准确且有用的筛选工具。
Artificial intelligence (AI) products can be trained to recognize tuberculosis (TB)-related abnormalities on chest radiographs. Various AI products are available commercially, yet there is lack of evidence on how their performance compared with each other and with radiologists. We evaluated five AI software products for screening and triaging TB using a large dataset that had not been used to train any commercial AI products. Individuals (>=15 years old) presenting to three TB screening centers in Dhaka, Bangladesh, were recruited consecutively. All CXR were read independently by a group of three Bangladeshi registered radiologists and five commercial AI products: CAD4TB (v7), InferReadDR (v2), Lunit INSIGHT CXR (v4.9.0), JF CXR-1 (v2), and qXR (v3). All five AI products significantly outperformed the Bangladeshi radiologists. The areas under the receiver operating characteristic curve are qXR: 90.81% (95% CI:90.33-91.29%), CAD4TB: 90.34% (95% CI:89.81-90.87), Lunit INSIGHT CXR: 88.61% (95% CI:88.03%-89.20%), InferReadDR: 84.90% (95% CI: 84.27-85.54%) and JF CXR-1: 84.89% (95% CI:84.26-85.53%). Only qXR met the TPP with 74.3% specificity at 90% sensitivity. Five AI algorithms can reduce the number of Xpert tests required by 50%, while maintaining a sensitivity above 90%. All AI algorithms performed worse among the older age and people with prior TB history. AI products can be highly accurate and useful screening and triage tools for TB detection in high burden regions and outperform human readers.
