PDF(Pubmed)
Abstract：
The use of video laryngoscopes has enhanced the 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 of anatomy. This study investigates the ability of an AI model to accurately identify the glottis in video laryngoscope images captured from a manikin. Tracheal intubation was conducted on a manikin using a bronchoscope with recording capabilities, and image data of the glottis was gathered for creating an AI model. Data preprocessing and annotation of the vocal cords, epiglottis, and glottis were performed, and human annotation of the vocal cords, epiglottis, and glottis was carried out. Based on the AI\'s determinations, anatomical structures were color-coded for identification. The recognition accuracy of the epiglottis and vocal cords recognized by the AI model was 0.9516, which was over 95%. The AI successfully marked the glottis, epiglottis, and vocal cords during the tracheal intubation process. These markings significantly aided in the visual identification of the respective structures with an accuracy of more than 95%. The AI demonstrated the ability to recognize the epiglottis, vocal cords, and glottis using an image recognition model of a manikin.
摘要：
视频喉镜的使用增强了声带的可视化，从而提高气管插管的可及性。利用人工智能（AI）来识别通过视频喉镜获得的图像，特别是在标记会厌和声带时，可以阐明解剖结构并增强对解剖学的理解。这项研究调查了AI模型在从人体模型捕获的视频喉镜图像中准确识别声门的能力。使用具有记录功能的支气管镜对人体模型进行气管插管，并收集声门的图像数据以创建AI模型。声带的数据预处理和注释，会厌,进行了声门检查，和人类对声带的注释，会厌,声门被执行。根据AI的判断，解剖结构采用颜色编码进行鉴定.AI模型对会厌和声带的识别准确率为0.9516,超过95%。人工智能成功地标记了声门，会厌,气管插管过程中的声带。这些标记显著地有助于相应结构的视觉识别,准确度超过95%。人工智能展示了识别会厌的能力，声带,和声门使用人体模型的图像识别。
