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

TitleA deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy.
Publication TypeJournal Article
Year of Publication2024
AuthorsYao P, Witte D, German A, Periyakoil P, Kim YEun, Gimonet H, Sulica L, Born H, Elemento O, Barnes J, Rameau A
JournalEur Arch Otorhinolaryngol
Volume281
Issue4
Pagination2055-2062
Date Published2024 Apr
ISSN1434-4726
KeywordsDeep Learning, Humans, Laryngoscopy, Machine Learning, Neural Networks, Computer, Polyps, Retrospective Studies, Vocal Cords
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.

DOI10.1007/s00405-023-08190-8
Alternate JournalEur Arch Otorhinolaryngol
PubMed ID37695363
PubMed Central ID7239848
Grant ListK76 AG079040 / AG / NIA NIH HHS / United States
OT2 OD032720 / CD / ODCDC CDC HHS / United States
K76 AG079040 / AG / NIA NIH HHS / United States
OT2 OD032720 / CD / ODCDC CDC HHS / United States