Linsen Sam, Kamoun Aurélie, Gunda Andrews, Mwenifumbo Tamara, Chavula Chancy, Nchimunya Lindiwe, Tsai Yucheng, Mulenga Namwaka, Kadewele Godfrey, Kajombo Eunice Nahache, Sunkutu Veronica, Shawa Jane, Kadam Rigveda, Arentz Matthew
FIND, Geneva, Switzerland.
Clinton Health Access Initiative, Lusaka, Zambia.
PLOS Digit Health. 2025 Jan 23;4(1):e0000535. doi: 10.1371/journal.pdig.0000535. eCollection 2025 Jan.
AI based software, including computer aided detection software for chest radiographs (CXR-CAD), was developed during the pandemic to improve COVID-19 case finding and triage. In high burden TB countries, the use of highly portable CXR and computer aided detection software has been adopted more broadly to improve the screening and triage of individuals for TB, but there is little evidence in these settings regarding COVID-19 CAD performance. We performed a multicenter, retrospective cross-over study evaluating CXRs from individuals at risk for COVID-19. We evaluated performance of CAD software and radiologists in comparison to COVID-19 laboratory results in 671 individuals evaluated for COVID-19 at sites in Zambia and Malawi between January 2021 and June 2022. All CXRs were interpreted by an expert radiologist and two commercially available COVID-19 CXR-CAD software. Radiologists interpreted CXRs for COVID-19 with a sensitivity of 73% (95% CI: 69%- 76%) and specificity of 49% (95% CI: 40%-58%). One CAD software (CAD2) showed performance in diagnosing COVID-19 that was comparable to that of radiologists, (AUC-ROC of 0.70 (95% CI: 0.65-0.75)), while a second (CAD1) showed inferior performance (AUC-ROC of 0.57 (95% CI: 0.52-0.63)). Agreement between CAD software and radiologists was moderate for diagnosing COVID-19, and agreement was very good in differentiating normal and abnormal CXRs in this high prevalent population. The study highlights the potential of CXR-CAD as a tool to support effective triage of individuals in Malawi and Zambia during the pandemic, particularly for distinguishing normal from abnormal CXRs. These findings suggest that while current AI-based diagnostics like CXR-CAD show promise, their effectiveness varies significantly. In order to better prepare for future pandemics, there is a need for representative training data to optimize performance in key populations, and ongoing data collection to maintain diagnostic accuracy, especially as new disease strains emerge.
基于人工智能的软件，包括用于胸部X光片的计算机辅助检测软件（CXR-CAD），在疫情期间被开发出来，以改善新冠病毒疾病（COVID-19）的病例发现和分流。在结核病高负担国家，更广泛地采用了高度便携的胸部X光片和计算机辅助检测软件，以改善结核病患者的筛查和分流，但在这些环境中，关于COVID-19计算机辅助检测性能的证据很少。我们进行了一项多中心回顾性交叉研究，评估有COVID-19感染风险个体的胸部X光片。我们在2021年1月至2022年6月期间，在赞比亚和马拉维的站点对671名接受COVID-19评估的个体，将计算机辅助检测软件和放射科医生的表现与COVID-19实验室结果进行了比较。所有胸部X光片均由一名专家放射科医生和两款商用COVID-19胸部X光片计算机辅助检测软件进行解读。放射科医生解读COVID-19胸部X光片的敏感度为73%（95%置信区间：69%-76%），特异度为49%（95%置信区间：40%-58%）。一款计算机辅助检测软件（CAD2）在诊断COVID-19方面的表现与放射科医生相当（曲线下面积-受试者工作特征曲线（AUC-ROC）为0.70（95%置信区间：0.65-0.75）），而另一款（CAD1）表现较差（AUC-ROC为0.57（95%置信区间：0.52-0.63））。计算机辅助检测软件和放射科医生在诊断COVID-19方面的一致性为中等，在区分该高流行人群中正常和异常胸部X光片方面的一致性非常好。该研究突出了胸部X光片计算机辅助检测作为一种工具在疫情期间支持马拉维和赞比亚有效分流个体的潜力，特别是在区分正常和异常胸部X光片方面。这些发现表明，虽然当前基于人工智能的诊断方法如胸部X光片计算机辅助检测显示出前景，但其有效性差异很大。为了更好地应对未来的疫情，需要有代表性的训练数据来优化关键人群中的表现，并持续收集数据以维持诊断准确性，特别是在新的疾病毒株出现时。
