Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network
- 1 February 2022
- journal article
- research article
- Published by Wiley in The Laryngoscope
- Vol. 132 (S4), S1-S8
- https://doi.org/10.1002/lary.28708
Abstract
Objectives/Hypothesis Create an autonomous computational system to classify endoscopy findings. Study Design Computational analysis of vocal fold images at an academic, tertiary-care laryngology practice. Methods A series of normal and abnormal vocal fold images were obtained from the image database of an academic tertiary care laryngology practice. The benign images included normals, nodules, papilloma, polyps, and webs. A separate set of carcinoma and leukoplakia images comprised a single malignant-premalignant class. All images were classified with their existing labels. Images were randomly withheld from each class for testing. The remaining images were used to train and validate a neural network for classifying vocal fold lesions. Two classifiers were developed. A multiclass system classified the five categories of benign lesions. A separate analysis was performed using a binary classifier trained to distinguish malignant-premalignant from benign lesions. Results Precision ranged from 71.7% (polyps) to 89.7% (papilloma), and recall ranged from 70.0% (papilloma) to 88.0% (nodules) for the benign classifier. Overall accuracy for the benign classifier was 80.8%. The binary classifier correctly identified 92.0% of the malignant-premalignant lesions with an overall accuracy of 93.0%. Conclusions Autonomous classification of endoscopic images with artificial intelligence technology is possible. Better network implementations and larger datasets will continue to improve classifier accuracy. A clinically useful optical cancer screening system may require a multimodality approach that incorporates nonvisual spectra. Level of Evidence NA Laryngoscope, 2020Funding Information
- Health Sciences Center New Orleans, Louisiana State University
This publication has 23 references indexed in Scilit:
- Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognitionComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2015
- Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural NetworksIEEE Journal of Biomedical and Health Informatics, 2015
- Accuracy of flexible versus rigid laryngoscopic photo-documentation in the diagnosis of early glottic cancerThe Journal of Laryngology & Otology, 2013
- Effective use of physician extenders in an outpatient otolaryngology settingThe Laryngoscope, 2011
- Detection of Lesions in Colonoscopic Images: A ReviewIFMBE Proceedings (IFMBE), 2009
- Raman spectroscopy for optical diagnosis in the larynx: Preliminary findingsLasers in Surgery and Medicine, 2005
- Optical biopsy: A new frontier in endoscopic detection and diagnosisClinical Gastroenterology and Hepatology, 2004
- Digital Dermoscopy Analysis and Artificial Neural Network for the Differentiation of Clinically Atypical Pigmented Skin Lesions: A Retrospective StudyJournal of Investigative Dermatology, 2002
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998
- Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.Radiology, 1993