Anatomical classification of pharyngeal and laryngeal endoscopic images using artificial intelligence

Background: The entire pharynx should be observed endoscopically to avoid missing pharyngeal lesions. An artificial intelligence (AI) model recognizing anatomical locations can help identify blind spots. We developed and evaluated an AI model classifying pharyngeal and laryngeal endoscopic locations. Methods: The AI model was trained using 5382 endoscopic images, categorized into 15 anatomical locations, and evaluated using an independent dataset of 1110 images. The main outcomes were model accuracy, precision, recall, and F1-score. Moreover, we investigated focused regions in the input images contributing to the model predictions using gradient-weighted class activation mapping (Grad-CAM) and Guided Grad-CAM. Results: Our AI model correctly classified pharyngeal and laryngeal images into 15 anatomical locations, with an accuracy of 93.3%. The weighted averages of precision, recall, and F1-score were 0.934, 0.933, and 0.933, respectively. Conclusion: Our AI model has an excellent performance determining pharyngeal and laryngeal anatomical locations, helping endoscopists notify of blind spots.