Detection of laryngeal carcinoma during endoscopy using artificial intelligence

Abstract Background The objective of this study was to assess the performance and application of a self‐developed deep learning (DL) algorithm for the real‐time localization and classification of both vocal cord carcinoma and benign vocal cord lesions. Methods The algorithm was trained and validated upon a dataset of videos and photos collected from our own department, as well as an open‐access dataset named “Laryngoscope8”. Results The algorithm correctly localizes and classifies vocal cord carcinoma on still images with a sensitivity between 71% and 78% and benign vocal cord lesions with a sensitivity between 70% and 82%. Furthermore, the best algorithm had an average frame per second rate of 63, thus making it suitable to use in an outpatient clinic setting for real‐time detection of laryngeal pathology. Conclusion We have demonstrated that our developed DL algorithm is able to localize and classify benign and malignant laryngeal pathology during endoscopy.