鉴于口腔癌的发病率不断增加，为高风险社区提供价格合理、用户友好的可视病变诊断工具尤为重要，特别是在偏远地区。该概念验证研究探索了一种可以拍摄和诊断口腔病变的智能手机应用的实用性和可行性。口腔病变的图像确认诊断来源于口腔颌面部教材。共提取了342张图像，包括来自口腔腔腭粘膜、腭部和唇部等口腔不同区域的病变。将这些病变分为三类：第一类代表非肿瘤性病变，第二类包括良性肿瘤，第三类包括癌前期/恶性病变。使用MobileNetV3和EfficientNetV2模型对图像进行了分析，该过程生成了准确率曲线、混淆矩阵和接收者操作特征曲线（ROC曲线）。 EfficientNetV2模型在迭代早期显示出验证准确率的显著增加，在得分达到0.71后趋于平稳。根据混淆矩阵，该模型对非肿瘤性和癌前期/恶性病变的诊断测试准确率分别为64%和80%。相反，MobileNetV3模型增长更为缓慢，在验证准确率达到0.70后趋于平稳。根据混淆矩阵，MobileNetV3模型对非肿瘤性和癌前期/恶性病变的诊断测试准确率分别为64%和82%。 我们的概念验证研究有效地展示了人工智能软件在区分恶性病变方面的潜在准确性。这在对口腔学专业人员有限的人群进行远程筛查中可能发挥重要作用。然而，图像分类和“非恶性病变”结果之间的差异要求进一步完善模型和使用的分类系统。 本研究的发现表明人工智能软件在识别或筛查恶性口腔病变方面具有潜力。需要进一步改进以提高对非恶性病变的分类准确性。 版权所有 © 2023。由Elsevier Ltd.出版。

Given the increasing incidence of oral cancer, it is essential to provide high-risk communities, especially in remote regions, with an affordable, user-friendly tool for visual lesion diagnosis. This proof-of-concept study explored the utility and feasibility of a smartphone application that can photograph and diagnose oral lesions.The images of oral lesions with confirmed diagnoses were sourced from oral and maxillofacial textbooks. In total, 342 images were extracted, encompassing lesions from various regions of the oral cavity such as the gingiva, palate, and labial mucosa. The lesions were segregated into three categories: Class 1 represented non-neoplastic lesions, Class 2 included benign neoplasms, and Class 3 contained premalignant/malignant lesions. The images were analysed using MobileNetV3 and EfficientNetV2 models, with the process producing an accuracy curve, confusion matrix, and receiver operating characteristic (ROC) curve.The EfficientNetV2 model showed a steep increase in validation accuracy early in the iterations, plateauing at a score of 0.71. According to the confusion matrix, this model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions was 64% and 80% respectively. Conversely, the MobileNetV3 model exhibited a more gradual increase, reaching a plateau at a validation accuracy of 0.70. The MobileNetV3 model's testing accuracy for diagnosing non-neoplastic and premalignant/malignant lesions, according to the confusion matrix, was 64% and 82% respectively.Our proof-of-concept study effectively demonstrated the potential accuracy of AI software in distinguishing malignant lesions. This could play a vital role in remote screenings for populations with limited access to dental practitioners. However, the discrepancies between the classification of images and the results of "non-malignant lesions" calls for further refinement of the models and the classification system used.The findings of this study indicate that AI software has the potential to aid in the identification or screening of malignant oral lesions. Further improvements are required to enhance accuracy in classifying non-malignant lesions.Copyright © 2023. Published by Elsevier Ltd.