Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa.
Qure.ai, Mumbai, India.
S Afr Med J. 2024 May 31;114(6):e1846. doi: 10.7196/SAMJ.2024.v114i6.1846.
Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. Settings with a high burden of tuberculosis (TB) and people living with HIV can potentially benefit from the use of AI to augment resource-constrained healthcare systems.
To assess the utility of qXR software (AI) in detecting radiological changes compatible with lung cancer or pulmonary TB (PTB).
We performed an observational study in a tertiary institution that serves a population with a high burden of lung cancer and PTB. In total, 382 chest radiographs that had a confirmed diagnosis were assessed: 127 with lung cancer, 144 with PTB and 111 normal. These chest radiographs were de-identified and randomly uploaded by a blinded investigator into qXR software. The output was generated as probability scores from predefined threshold values.
The overall sensitivity of the qXR in detecting lung cancer was 84% (95% confidence interval (CI) 80 - 87%), specificity 91% (95% CI 84 - 96%) and positive predictive value of 97% (95% CI 95 - 99%). For PTB, it had a sensitivity of 90% (95% CI 87 - 93%) and specificity of 79% (95% CI 73 - 84%) and negative predictive value of 85% (95% CI 79 - 91%).
The qXR software was sensitive and specific in categorising chest radiographs as consistent with lung cancer or TB, and can potentially aid in the earlier detection and management of these diseases.
人工智能（AI）利用深度学习（DL）系统，可以用于检测各种肺部疾病的放射学变化。结核病（TB）负担重的地区和艾滋病毒感染者可能受益于 AI 的使用，以增强资源有限的医疗保健系统。
评估 qXR 软件（AI）检测与肺癌或肺结核（PTB）相符的放射学变化的效用。
我们在一家为肺癌和 PTB 负担重的人群服务的三级机构中进行了一项观察性研究。共评估了 382 张确诊的胸部 X 光片：127 张肺癌，144 张肺结核，111 张正常。这些胸部 X 光片被匿名化并由一名盲法调查员随机上传到 qXR 软件中。输出结果是根据预定义阈值生成的概率分数。
qXR 检测肺癌的总体敏感性为 84%（95%置信区间 80-87%），特异性为 91%（95%置信区间 84-96%），阳性预测值为 97%（95%置信区间 95-99%）。对于肺结核，其敏感性为 90%（95%置信区间 87-93%），特异性为 79%（95%置信区间 73-84%），阴性预测值为 85%（95%置信区间 79-91%）。
qXR 软件在将胸部 X 光片分类为与肺癌或 TB 一致方面具有较高的敏感性和特异性，可用于辅助早期发现和管理这些疾病。
