Nakamura Hiroki, Fukuda Kouki, Asano Takahiko, Masue Tatsuhiko
Pain Center, Kouseikai Takai Hospital, Nara, Japan; NCD Epidemiology Research Center, Shiga University of Medical Science, Shiga, Japan.
Department of Anesthesiology, Gifu Prefectural General Medical Center, Gifu, Japan.
J Clin Anesth. 2025 Sep;106:111914. doi: 10.1016/j.jclinane.2025.111914. Epub 2025 Jun 25.
The larynx of infants and neonates is occasionally challenging to identify using video laryngoscope. This study aimed to develop an artificial intelligence (AI)-assisted model that can identify the larynx, including the opening vocal cord and the arytenoid portion, on images obtained using a video laryngoscope.
First, 1197 images were extracted by the author from the 653 videos for train and validation data. An AI-assisted model for identifying the larynx was developed using YOLOv8n. Then, 399 images were selected from the additional 63 videos for the test data at every 150 frames.
The sensitivity, specificity, and area under the curve of the AI-assisted model for identifying the larynx were 0.74, 0.99, and 0.91, respectively. The AI model correctly identified the opening vocal cord and arytenoid portion in cases without obstacles. Esophageal misidentification of the larynx and undetected cases of the larynx caused by obstacles were observed.
The AI-assisted model effectively identified the larynx, including the opening vocal cord and the arytenoid portion, during video laryngoscopy and potentially can enhance the safety of tracheal intubation of infants and neonates. However, esophageal misidentification remains critical. Further studies are needed to refine the model and ensure its reliability in clinical practice.
