Background: Fish bone impaction is one of the most common problems encountered in otolaryngology emergencies. Due to their small and transparent nature, as well as the complexity of pharyngeal anatomy, identifying fish bones efficiently under laryngoscopy requires substantial clinical experience. This study aims to create an AI model to assist clinicians in detecting pharyngeal fish bones more efficiently under laryngoscopy.
Methods: Totally 3133 laryngoscopic images related to fish bones were collected for model training and validation. The images in the training dataset were trained using the YOLO-V5 algorithm model. After training, the model was validated and its performance was evaluated using a test dataset. The model's predictions were compared to those of human experts. Seven laryngoscopic videos related to fish bone were used to validate real-time target detection by the model.
Results: The model trained in YOLO-V5 demonstrated good generalization and performance, with an average precision of 0.857 when the intersection over union (IOU) threshold was set to 0.5. The precision, recall rate, and F1 scores of the model are 0.909, 0.818, and 0.87, respectively. The overall accuracy of the model in the validation set was 0.821, comparable to that of ENT specialists. The model processed each image in 0.012 s, significantly faster than human processing (p < 0.001). Furthermore, the model exhibited outstanding performance in video recognition.
Conclusion: Our AI model based on YOLO-V5 effectively identifies and localizes fish bone foreign bodies in static laryngoscopic images and dynamic videos. It shows great potential for clinical application.
Level of evidence: 3 Laryngoscope, 134:2162-2169, 2024.
Keywords: YOLO; computer vision; deep learning; fish bones; laryngoscopy.
© 2023 The American Laryngological, Rhinological and Otological Society, Inc.