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Nasogastric and endotracheal tubes are widely commonly used tools to aid in the care of critical ill patients such as in nutrition, decompression, and in emergently situations to manage respiratory failure. A reliable model to detect early appropriate NG/ET positioning can provide an aid for clinicians in remote areas to guide interventions, or also to potentially note changes in tubes positions when chest x-rays might be otherwise overlooked. The creation and efficacy of an early detection tool to identify appropriate position of these tubes via CXR using machine learning is unknown