The outcomes of our study firmly establish the efficacy of employing a deep learning model, augmented with active learning techniques, for the detection of nasogastric tubes in chest X-rays. This advancement not only underscores the potential of artificial intelligence in medical diagnostics but also marks a significant step forward in enhancing patient safety. The development and implementation of a classifier for nasogastric tube position detection promise to be a valuable tool in the medical field, offering a more reliable, efficient, and safer approach to a critical healthcare procedure. Ultimately, our study lays the groundwork for future research and development in the realm of medical imaging and artificial intelligence, opening new avenues for innovative healthcare solutions.