Enhancing Intubation Accuracy: Advanced Tracheal Segmentation Techniques In Video Endoscopy

Abstract

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Tracheal intubation is a critical medical procedure involving the insertion of a tube into the trachea to maintain an open airway. While essential, this procedure carries significant risks, such as incorrect tube placement. Advances in visually guided intubation methods, like video laryngoscopy, have enhanced safety by enabling precise tracheal segmentation from endoscopic images. Our study introduces an innovative image enhancement technique for video endoscopy that significantly improves tracheal visibility and segmentation accuracy. This novel approach not only facilitates safer and more accurate intubation but also minimizes patient discomfort and procedural risks. Tested against the UoS Dataset and real patient data from thyroidectomy procedures, our method demonstrated superior performance, achieving a segmentation accuracy of $97 \%$, a precision of $94 \%$, and a recall of $99 \%$. Our tailored method is computationally efficient, making it suitable for implementation on edge devices like Arduino, thereby enhancing intubation safety and efficiency in various medical settings.