Detection of laryngeal carcinoma during endoscopy using artificial intelligence.

Autor: Wellenstein DJ; Department of Otorhinolaryngology and Head and Neck Surgery, Radboud University Medical Center, Nijmegen, The Netherlands., Woodburn J; WSK Medical B.V., Amsterdam, The Netherlands., Marres HAM; Department of Otorhinolaryngology and Head and Neck Surgery, Radboud University Medical Center, Nijmegen, The Netherlands., van den Broek GB; Department of Otorhinolaryngology and Head and Neck Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.; Department of Information Management, Radboud University Medical Center, Nijmegen, The Netherlands.
Jazyk: angličtina
Zdroj: Head & neck [Head Neck] 2023 Sep; Vol. 45 (9), pp. 2217-2226. Date of Electronic Publication: 2023 Jun 28.
DOI: 10.1002/hed.27441
Abstrakt: 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.
(© 2023 The Authors. Head & Neck published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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