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

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.

Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis

“…This analysis included 14 studies [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Table 1 presents the assessment of bias.…”

“… Forest plot of the diagnostic odds ratios for ( A ) screening only oral cancerous lesions [ 13 , 16 , 17 , 21 , 22 , 23 , 25 ] and ( B ) screening all premalignant mucosal lesions [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 ]. …”

“… Forest plots of ( A ) sensitivity, ( B ) specificity, and ( C ) negative predictive values for screening all premalignant mucosal lesions [ 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 ]. …”

Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images

“…These results were strongly suggestive of automated oral cancer detection on OCT images. James et al [ 111 ] implemented artificial neural networks and a support vector machine model to annotate image features of OCT images obtained from the normal oral mucosa and benign and malignant lesions ( Figure 1 ).…”

Miniaturizing optical coherence tomography

“…This flexibility opens up the possibility to image the retina of immobile/bedridden patients or infants, skin areas that are not accessible easily, ear-nose-throat measurements, or even large animals [31] and plants [32] can be imaged. In the last decade, handheld probes have proven their value in imaging the retina [33][34][35][36][37][38][39][40][41][42][43][44][45][46], hair follicles [47] and the tympanic membrane in the ear [30,48,49], to detect and monitor skin cancer [50,51] or skin and mucosal lesions [52][53][54][55][56][57][58], and as an imaging tool to assist in surgery [59,60].…”