Abstract
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.
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Funding
This project was supported by the American Laryngological Voice and Research Education Grant. Anaïs Rameau was supported by a Paul B. Beeson Emerging Leaders Career Development Award in Aging (K76 AG079040) from the National Institute on Aging and by the Bridge2AI award (OT2 OD032720) from the NIH Common Fund. Anaïs Rameau is a medical advisor for Perceptron Health, Inc. Dan Witte is a co-founder of Perceptron Health, Inc.
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Weill Cornell Medical College IRB approval was obtained for this study, Protocol # 19-05020151. Ethical Standards were upheld by all authors.
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Informed consent was not required by the IRB due to the retrospective nature of the clinical data used in this study.
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Yao, P., Witte, D., German, A. et al. A deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy. Eur Arch Otorhinolaryngol 281, 2055–2062 (2024). https://doi.org/10.1007/s00405-023-08190-8
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DOI: https://doi.org/10.1007/s00405-023-08190-8