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.

中文翻译：

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使用光学相干断层扫描和卷积神经网络来区分正常和异常口腔粘膜。

头颈部鳞状细胞癌（HNSCC）手术切除不完全是局部HNSCC复发的最常见原因。当前，外科医生依靠术前成像，直接可视化，触诊和冰冻切片来确定组织切除的程度。已经证明，光学相干断层扫描（OCT）是一种微创的，非电离的近红外介观成像方式，可以解决正常与异常头颈部粘膜之间的表面下差异。先前的工作使用了二维OCT成像，该成像仅限于逐帧生成的小目标区域的评估。OCT技术能够执行快速体积成像，但是缺乏分析大量图像数据的能力和专业知识。在这项研究中，我们评估了经过训练的卷积神经网络对头颈部粘膜的三维OCT图像进行分类以区分正常组织和异常组织的能力，其敏感性和特异性分别为100％和70％。该方法有潜力作为评估手术切缘的实时分析工具。