A deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy.
| Publication Type | Academic Article |
| Authors | Yao P, Witte D, German A, Periyakoil P, Kim Y, Gimonet H, Sulica L, Born H, Elemento O, Barnes J, Rameau A |
| Journal | Eur Arch Otorhinolaryngol |
| Volume | 281 |
| Issue | 4 |
| Pagination | 2055-2062 |
| Date Published | 09/11/2023 |
| ISSN | 1434-4726 |
| Keywords | Deep Learning, Polyps |
| 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. |
| DOI | 10.1007/s00405-023-08190-8 |
| PubMed ID | 37695363 |