Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Department of Oral Diagnosis, Marmara University, Başıbüyük Sağlık Yerleşkesi Başıbüyük Yolu 9/3 34854 Maltepe, İstanbul, Turkey.
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey.
J Stomatol Oral Maxillofac Surg. 2023 Feb;124(1):101264. doi: 10.1016/j.jormas.2022.08.007. Epub 2022 Aug 11.
Deep learning methods have recently been applied for the processing of medical images, and they have shown promise in a variety of applications. This study aimed to develop a deep learning approach for identifying oral lichen planus lesions using photographic images.
Anonymous retrospective photographic images of buccal mucosa with 65 healthy and 72 oral lichen planus lesions were identified using the CranioCatch program (CranioCatch, Eskişehir, Turkey). All images were re-checked and verified by Oral Medicine and Maxillofacial Radiology experts. This data set was divided into training (n = 51; n = 58), verification (n = 7; n = 7), and test (n = 7; n = 7) sets for healthy mucosa and mucosa with the oral lichen planus lesion, respectively. In the study, an artificial intelligence model was developed using Google Inception V3 architecture implemented with Tensorflow, which is a deep learning approach.
AI deep learning model provided the classification of all test images for both healthy and diseased mucosa with a 100% success rate.
In the healthcare business, AI offers a wide range of uses and applications. The increased effort increased complexity of the job, and probable doctor fatigue may jeopardize diagnostic abilities and results. Artificial intelligence (AI) components in imaging equipment would lessen this effort and increase efficiency. They can also detect oral lesions and have access to more data than their human counterparts. Our preliminary findings show that deep learning has the potential to handle this significant challenge.
深度学习方法最近已被应用于医学图像的处理，并且在各种应用中显示出了很大的潜力。本研究旨在开发一种基于摄影图像识别口腔扁平苔藓病变的深度学习方法。
使用 CranioCatch 程序（CranioCatch，土耳其埃斯基谢希尔）鉴定了 65 例健康和 72 例口腔扁平苔藓病变的颊黏膜匿名回顾性摄影图像。所有图像均由口腔医学和颌面放射学专家重新检查和验证。该数据集分为健康黏膜（n=51；n=58）和口腔扁平苔藓病变黏膜（n=7；n=7）的训练集、验证集和测试集。研究中，使用基于 Google Inception V3 架构的 Tensorflow 实现了人工智能模型，这是一种深度学习方法。
AI 深度学习模型对所有测试图像进行分类，对健康和患病黏膜的分类成功率均为 100%。
在医疗保健业务中，人工智能提供了广泛的用途和应用。工作复杂性的增加和可能的医生疲劳可能会危及诊断能力和结果。成像设备中的人工智能组件将减轻这一负担并提高效率。它们还可以检测口腔病变，并比人类获得更多的数据。我们的初步研究结果表明，深度学习有可能应对这一重大挑战。
