In:
The Laryngoscope, Wiley, Vol. 130, No. 11 ( 2020-11)
Abstract:
To develop a deep‐learning–based computer‐aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician‐based accuracy of diagnostic assessments of laryngoscopy findings. Study Design Retrospective study. Methods A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)‐based classifier. A comparison between the proposed CNN‐based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN‐based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P 〈 .001), polyps (91% vs. 86%, P 〈 .001), leukoplakia (91% vs. 65%, P 〈 .001), and malignancy (90% vs. 54%, P 〈 .001). Conclusions The CNN‐based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions. Level of Evidence NA Laryngoscope , 130:E686–E693, 2020
Type of Medium:
Online Resource
ISSN:
0023-852X
,
1531-4995
DOI:
10.1002/lary.v130.11
Language:
English
Publisher:
Wiley
Publication Date:
2020
detail.hit.zdb_id:
2026089-1
