The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

Incomplete surgical resection of head and neck squamous cell carcinoma (HNSCC) is the most common cause of local HNSCC recurrence. Currently, surgeons rely on preoperative imaging, direct visualization, palpation and frozen section to determine the extent of tissue resection. It has been demonstrated that optical coherence tomography (OCT), a minimally invasive, nonionizing near infrared mesoscopic imaging modality can resolve subsurface differences between normal and abnormal head and neck mucosa. Previous work has utilized two‐dimensional OCT imaging which is limited to the evaluation of small regions of interest generated frame by frame. OCT technology is capable of performing rapid volumetric imaging, but the capacity and expertise to analyze this massive amount of image data is lacking. In this study, we evaluate the ability of a retrained convolutional neural network to classify three‐dimensional OCT images of head and neck mucosa to differentiate normal and abnormal tissues with sensitivity and specificity of 100% and 70%, respectively. This method has the potential to serve as a real‐time analytic tool in the assessment of surgical margins. Successful surgical treatment of head and neck squamous cell carcinoma (HNSCC) relies on margins clear of tumor. A pre‐existing convolutional neural network (CNN) was retrained on histologically coregistered optical coherence tomography (OCT) images of HNSCC surgical margins to screen nonlabeled OCT data. Accuracy of the CNN was assessed on nine patients undergoing tumor surgical resection. The retrained CNN is capable of classifying three‐dimensional OCT images of head and neck mucosa as normal and abnormal with sensitivity and specificity of 100% and 70%, respectively.