Detecting oropharyngeal carcinoma using multispectral, narrow-band imaging and machine learning.

Journal: The Laryngoscope

Published Date: Nov 1, 2018

Abstract

OBJECTIVE: To determine if multispectral narrow-band imaging (mNBI) can be used for automated, quantitative detection of oropharyngeal carcinoma (OPC).

Authors

Shamik Mascharak

Division of Head and Neck Surgery, Department of Otolaryngology, School of Medicine, Stanford University, Palo Alto, California, U.S.A.
Brandon J Baird

Division of Head and Neck Surgery, Department of Otolaryngology, School of Medicine, Stanford University, Palo Alto, California, U.S.A.
F Christopher Holsinger

Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.

Keywords

Aged Aged, 80 and over Bayes Theorem Carcinoma Diagnosis, Differential Female Humans Machine Learning Male Middle Aged Narrow Band Imaging Oropharyngeal Neoplasms Pattern Recognition, Automated Prospective Studies Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (29577322)

Detecting oropharyngeal carcinoma using multispectral, narrow-band imaging and machine learning.

Abstract

Authors

Keywords

External Resources

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