Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Department of Oral Diagnosis and Başıbüyük Sağlık, Marmara University, Yerleşkesi Başıbüyük Yolu 9/3 34854, Maltepe, İstanbul, Turkey.
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Department of Oral Diagnosis and Başıbüyük Sağlık, Marmara University, Yerleşkesi Başıbüyük Yolu 9/3 34854, Maltepe, İstanbul, Turkey.
J Stomatol Oral Maxillofac Surg. 2024 Oct;125(5S2):101975. doi: 10.1016/j.jormas.2024.101975. Epub 2024 Jul 21.
Oral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer lesions in intra-oral retrospective patient images.
Oral cancer lesions were labeled with CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) and polygonal type labeling method on a total of 65 anonymous retrospective intraoral patient images of oral mucosa that were diagnosed with oral cancer histopathologically by incisional biopsy from individuals in our clinic. All images have been rechecked and verified by experienced experts. This data set was divided into training (n = 53), validation (n = 6) and test (n = 6) sets. Artificial intelligence model was developed using YOLOv5 architecture, which is a deep learning approach. Model success was evaluated with confusion matrix.
When the success rate in estimating the images reserved for the test not used in education was evaluated, the F1, sensitivity and precision results of the artificial intelligence model obtained using the YOLOv5 architecture were found to be 0.667, 0.667 and 0.667, respectively.
Our study reveals that OCSCC lesions carry discriminative visual appearances, which can be identified by deep learning algorithm. Artificial intelligence shows promise in the prediagnosis of oral cancer lesions. The success rates will increase in the training models of the data set that will be formed with more images.
口腔内的口腔鳞状细胞癌（OSCC）是一类疾病，牙医可以诊断甚至治愈。本研究评估了为检测口腔癌病变而开发的诊断计算机软件在口腔内回顾性患者图像中的性能。
使用 CranioCatch 标记程序（CranioCatch，土耳其埃斯基谢希尔）和多边形类型标记方法对 65 张匿名回顾性口腔黏膜内口腔癌患者图像进行标记，这些图像通过我们诊所的个体进行切开活检被诊断为口腔癌组织病理学。所有图像均经过重新检查和验证。该数据集分为训练集（n = 53）、验证集（n = 6）和测试集（n = 6）。使用 YOLOv5 架构开发人工智能模型，这是一种深度学习方法。使用混淆矩阵评估模型的成功。
当评估未在教育中使用的保留图像的估计成功率时，使用 YOLOv5 架构获得的人工智能模型的 F1、灵敏度和精度结果分别为 0.667、0.667 和 0.667。
我们的研究表明，口腔鳞状细胞癌病变具有可识别的视觉外观，可以通过深度学习算法识别。人工智能在口腔癌病变的预诊断中具有潜力。随着更多图像形成的数据集的训练模型的增加，成功率将会提高。
