Determining Carina and Clavicular Distance-Dependent Positioning of Endotracheal Tube in Critically Ill Patients: An Artificial Intelligence-Based Approach

SIMPLE SUMMARY: Endotracheal intubation (ETI) is employed for maintaining the airway patency of in critically ill patients during mechanical ventilation. Generally, ETI is conducted under anesthesia in the intensive care unit, during which the endotracheal tube (ETT) is inserted at a particular depth into the trachea, and a malpositioned ETT may result in hazardous consequences, such as a collapsed or hyperinflated lung. Therefore, we proposed a deep learning-based CNN approach, combined with four key point annotations on chest radiographs (tracheal tube end, carina, and left/right clavicular heads), which demonstrated significant sensitivity, specificity, and accuracy for recognizing and localizing the ETT tip on chest radiographs. These findings may assist in radiographic confirmation of precise ETT placement and help in ruling out other etiologies of respiratory failure. ABSTRACT: Early and accurate prediction of endotracheal tube (ETT) location is pivotal for critically ill patients. Automatic and timely detection of faulty ETT locations from chest X-ray images may avert patients’ morbidity and mortality. Therefore, we designed convolutional neural network (CNN)-based algorithms to evaluate ETT position appropriateness relative to four detected key points, including tracheal tube end, carina, and left/right clavicular heads on chest radiographs. We estimated distances from the tube end to tracheal carina and the midpoint of clavicular heads. A DenseNet121 encoder transformed images into embedding features, and a CNN-based decoder generated the probability distributions. Based on four sets of tube-to-carina distance-dependent parameters (i.e., (i) 30–70 mm, (ii) 30–60 mm, (iii) 20–60 mm, and (iv) 20–55 mm), corresponding models were generated, and their accuracy was evaluated through the predicted L1 distance to ground-truth coordinates. Based on tube-to-carina and tube-to-clavicle distances, the highest sensitivity, and specificity of 92.85% and 84.62% respectively, were revealed for 20–55 mm. This implies that tube-to-carina distance between 20 and 55 mm is optimal for an AI-based key point appropriateness detection system and is empirically comparable to physicians’ consensus.