Performance validation of an artificial intelligence-assisted chest radiograph algorithm for incidental pulmonary nodule detection in Malaysian healthcare facilities: a multicentre cross-sectional study protocol

Incidental pulmonary nodules (IPNs) are frequently detected on chest X-rays (CXRs) performed for routine clinical indications and may represent early-stage lung cancer. While low-dose CT (LDCT) remains limited in resource -constrained settings such as Malaysia. In this context, AI-assisted CXR interpretation offers a scalable approach for opportunistic detection of lung nodules, though local validation is essential for clinical adoption.

This prospective, multicenter cross-sectional study aims to evaluate the diagnostic performance of an AI-based solution, qXR, across hospitals in the Klang Valley region. Approximately 1,000-2,000 consecutive adult CXRs will be analyzed for detection of nodules ≥6 mm. Ground truth will be established through independent review by two thoracic radiologists, with CT correlation where available. 

Primary outcomes include sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Secondary analyses will assess performance across nodule size and location, as well as the impact on radiologist reading time. Statistical methods include ROC curve analysis and paired comparison using McNemar testing.

Although results are pending, prior evidence suggests AI sensitivity of ~83-90% for nodule detection, with a 9-10% improvement when used as a second reader. This study is expected to provide critical real-world validation in a multi-ethnic population and demonstrate how AI-enabled CXRs can serve as a scalable alternative to LDCT, supporting earlier lung cancer detection in underserved regions globally.