AI-Assisted Nasogastric Tube Placement Verification Methods Examined

Because the misplacement of nasogastric tubes poses serious health risks, the potential of AI-based image analysis systems to improve this process has been scrutinized through a systematic review.

Although nasogastric tubes (NGTs) play a critical role in enteral nutrition and medication administration, their misplacement brings life-threatening risks such as aspiration and pneumothorax. The NHS classifies erroneous placements into the respiratory or pleural cavity as a 'Never Event'. Current verification methods such as gastric aspirate testing or chest radiography (CXR) can present difficulties due to interpretation differences among clinical staff and delays in the process.

Registered on the Open Science Framework (OSF) and following PRISMA 2020 and PRISMA-DTA guidelines, this systematic review investigates the effectiveness of artificial intelligence and deep learning systems in determining tube position on radiographic images. Prepared through searches conducted in databases such as PubMed, Embase, IEEE Xplore, and the NHS Knowledge and Library Hub up to September 1, 2026, the study aims to determine whether transitioning to clinical practice is possible by analyzing technical parameters such as the diagnostic performance of AI technologies, external validation processes, and class imbalance.