Review of Artificial Intelligence for Automated Assessment of Lines, Drains, and Airways on Pediatric and Adult Chest Radiographs

The accurate placement of lines, drains, and airways (LDAs) is essential for the safe management of critically ill patients, as malpositioned medical devices can result in severe complications ranging from ineffective treatment to life-threatening injury. Chest radiography (CXR) is the primary imaging modality for confirming the placement of many LDAs; however, the growing volume and complexity of bedside CXRs have created increasing demand for rapid, reliable interpretation. Recent advances in artificial intelligence (AI) have enabled automated detection, localization, and position assessment of LDA devices, supporting opportunities to improve clinical workflow and patient safety. This review summarizes recent developments in AI algorithms for automated LDA assessment on CXRs, emphasizing their clinical applications, technical approaches, performance, and limitations. The review begins with clinical characteristics, radiographic appearance, and placement criteria of common LDA categories, followed by a survey of AI methods for presence detection, device localization, and position classification. Although some models have achieved performance approaching that of radiologists, most do not perform at the level needed for clinical deployment. Future research should prioritize multicenter validation, standardized annotation protocols, pediatric-specific datasets, and anatomically informed models capable of simultaneously evaluating multiple LDA devices. Advances in these areas will facilitate the integration of AI-assisted LDA assessment into clinical decision support and ultimately improve patient care.