Yang Zihan, Pan Hongming, Shang Jianwei, Zhang Jun, Liang Yanmei
Institute of Modern Optics, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin 300350, China.
Department of Oral Pathology, Tianjin Stomatological Hospital, Hospital of Stomatology, Nankai University, Tianjin 300041, China.
Biomedicines. 2023 Mar 6;11(3):802. doi: 10.3390/biomedicines11030802.
Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics (accuracy, precision, sensitivity, and specificity). Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer.
口腔癌的早期检测和诊断对于改善预后至关重要，但使用现有技术进行准确和自动的识别却很困难。由于具有高分辨率和非侵入性的优点，光学相干断层扫描（OCT）可作为诊断辅助手段。我们旨在评估基于深度学习的OCT图像算法，以协助临床医生进行口腔癌筛查和诊断。首先建立了一个OCT数据集，包括正常黏膜、癌前病变和口腔鳞状细胞癌。然后，使用四种指标（准确率、精确率、灵敏度和特异性）对三种卷积神经网络（CNN）进行训练和评估。此外，通过同一数据集将基于CNN的方法与机器学习方法进行比较。结果表明，CNN的性能优于基于机器学习的方法，其分类准确率高达96.76%，而基于机器学习的方法准确率为92.52%。此外，还对OCT图像中的病变进行了可视化，并评估了模型区分不同口腔组织的合理性和可解释性。事实证明，基于深度学习的OCT图像自动识别算法有潜力为口腔癌的有效筛查和诊断提供决策支持。
