Inicio  /  Cancers  /  Vol: 13 Par: 14 (2021)  /  Artículo
ARTÍCULO
TITULO

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions

Bonney Lee James    
Sumsum P. Sunny    
Andrew Emon Heidari    
Ravindra D. Ramanjinappa    
Tracie Lam    
Anne V. Tran    
Sandeep Kankanala    
Shiladitya Sil    
Vidya Tiwari    
Sanjana Patrick    
Vijay Pillai    
Vivek Shetty    
Naveen Hedne    
Darshat Shah    
Nameeta Shah    
Zhong-ping Chen    
Uma Kandasarma    
Subhashini Attavar Raghavan    
Shubha Gurudath    
Praveen Birur Nagaraj    
Petra Wilder-Smith    
Amritha Suresh and Moni Abraham Kuriakoseadd Show full author list remove Hide full author list    

Resumen

Early detection is crucial towards improving survival in patients diagnosed with oral cancer. Non-invasive strategies equivalent to histology diagnosis are extremely valuable in oral cancer screening and early detection in resource-constrained settings. Optical coherence tomography (OCT), an optical biopsy technique enables real-time imaging with periodic surveillance and capability to image architectural features of the tissues. We report that while OCT system delineates oral pre-cancer and cancer with more than 90% sensitivity, integration, with artificial neural network-based analysis efficiently identifies high-risk, oral pre-cancer (83%). This study provides evidence that the robust, low-cost system was effective as a point-of-care device in resource-constrained settings. The high accuracy and portability signify widespread clinical application in oral cancer screening and/or surveillance.

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