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 E. 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, Zhongping Chen, Uma Kandasarma, Subhashini Raghavan, Shubha Gurudath, Praveen Birur Nagaraj, Petra Wilder‐Smith, Amritha Suresh, Moni Abraham Kuriakose
Abstract
Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer.