A deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy

• Published in: European archives of oto-rhino-laryngology 2024-04, Vol.281 (4), p.2055-2062
• Main Authors: Yao, Peter, Witte, Dan, German, Alexander, Periyakoil, Preethi, Kim, Yeo Eun, Gimonet, Hortense, Sulica, Lucian, Born, Hayley, Elemento, Olivier, Barnes, Josue, Rameau, Anaïs
• Format: Article
• Language: English
• Subjects: Deep Learning; Head and Neck Surgery; Humans; Laryngology; Laryngoscopy - methods; Machine Learning; Medicine; Medicine & Public Health; Neural Networks, Computer; Neurosurgery; Otorhinolaryngology; Polyps - diagnostic imaging; Retrospective Studies; Vocal Cords - diagnostic imaging
• ISSN: 0937-4477; 1434-4726
• DOI: 10.1007/s00405-023-08190-8

Purpose To develop and validate a deep learning model for distinguishing healthy vocal folds (HVF) and vocal fold polyps (VFP) on laryngoscopy videos, while demonstrating the ability of a previously developed informative frame classifier in facilitating deep learning development. Methods Following retrospective extraction of image frames from 52 HVF and 77 unilateral VFP videos, two researchers manually labeled each frame as informative or uninformative. A previously developed informative frame classifier was used to extract informative frames from the same video set. Both sets of videos were independently divided into training (60%), validation (20%), and test (20%) by patient. Machine-labeled frames were independently verified by two researchers to assess the precision of the informative frame classifier. Two models, pre-trained on ResNet18, were trained to classify frames as containing HVF or VFP. The accuracy of the polyp classifier trained on machine-labeled frames was compared to that of the classifier trained on human-labeled frames. The performance was measured by accuracy and area under the receiver operating characteristic curve (AUROC). Results When evaluated on a hold-out test set, the polyp classifier trained on machine-labeled frames achieved an accuracy of 85% and AUROC of 0.84, whereas the classifier trained on human-labeled frames achieved an accuracy of 69% and AUROC of 0.66. Conclusion An accurate deep learning classifier for vocal fold polyp identification was developed and validated with the assistance of a peer-reviewed informative frame classifier for dataset assembly. The classifier trained on machine-labeled frames demonstrates improved performance compared to the classifier trained on human-labeled frames. Level of evidence 4.