Glottis recognition software development using artificial intelligence

Abstract

Purpose The use of video laryngoscopes has enhanced visualization of the vocal cords, thereby improving the accessibility of tracheal intubation. Employing artificial intelligence (AI) to recognize images obtained through video laryngoscopy, particularly when marking the epiglottis and vocal cords, may elucidate anatomical structures and enhance anatomical comprehension. This study investigated the ability of an AI model to accurately identify the glottis on video laryngoscopy images captured from a human anatomical model. Methods Tracheal intubation was conducted on a human anatomical model using a bronchoscope with recording capabilities, and image data of the glottis were gathered to create an AI model. Data preprocessing and annotation of the vocal cords, epiglottis, and glottis were performed. Based on the AI determinations, anatomical structures were color-coded for identification. Results The accuracy of the AI model was 0.9516, which exceeded 95%. AI successfully marked the glottis, epiglottis, and vocal cords during tracheal intubation. These markings aided in the visual identification of the respective structures with an accuracy of more than 95%. Conclusion AI demonstrated the ability to recognize the epiglottis, vocal cords, and glottis using an image recognition model of a human anatomical model.