Presentation
7 March 2022 Machine-learning driven automated discrimination of precancerous and cancerous from benign oral lesions using multispectral autofluorescence imaging endoscopy
Author Affiliations +
Abstract
Multispectral autofluorescence endoscopy is a non-invasive optical imaging modality that can provide contrast between malignant and benign oral tissue. We hypothesized that discrimination of cancerous and precancerous from benign oral lesions can be achieved through machine-learning (ML) models developed with multispectral autofluorescence intensity features. In vivo multispectral autofluorescence endoscopic images of benign, precancerous, and cancerous oral lesions were acquired from 67 patients and used to optimize ML models for discrimination between cancerous/precancerous and benign lesions. This study demonstrates the potentials of a ML-assisted system based on multispectral autofluorescence endoscopy for automated discrimination of cancerous and precancerous from benign oral lesions.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Elvis Duran, Shuna Cheng, Rodrigo Cuenca, Beena Ahmed, Jim Ji, Vladislav V. Yakovlev, Mathias Martinez, Moustafa Al-Khalil, Hussain Al-Enazi, Y.S. Lisa Cheng, John Wright, Carlos Busso, and Javier Jo "Machine-learning driven automated discrimination of precancerous and cancerous from benign oral lesions using multispectral autofluorescence imaging endoscopy", Proc. SPIE PC11935, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2022, PC1193507 (7 March 2022); https://doi.org/10.1117/12.2608843
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KEYWORDS
Endoscopy

Multispectral imaging

Auto-fluorescence imaging

Cancer

Biopsy

In vivo imaging

Optical imaging

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