Department of Stomatology, Bauru Dental School, USP - University of São Paulo, Bauru, SP, Brazil.
Department of Stomatology, Bauru Dental School, USP - University of São Paulo, Bauru, SP, Brazil.
Oral Oncol. 2022 Nov;134:106117. doi: 10.1016/j.oraloncology.2022.106117. Epub 2022 Sep 12.
Oral cancer could be prevented. The primary strategy is based on prevention. Most patients with oral cancer present to the hospital network with advanced staging and a low chance of cure. This condition may be related to physicians' difficulty of making an early diagnosis. With the advancement of information technology, artificial intelligence (AI) holds great promise in terms of assisting in diagnosis. Few machine learning algorithms have been developed for this purpose to date. In this paper, we will discuss the possibilities for diagnosing oral cancer using AI as a tool, as well as the implications for the population. A set of photographic images of oral lesions has been segmented, indicating not only the area of the lesion but also the class of lesion associated with it. Different neural network architectures were trained with the goal of fine segmentation (pixel by pixel), classification of image crops, and classification of whole images based on the presence or absence of a lesion. The accuracy results are acceptable, opening up possibilities not only for identifying lesions but also for classifying the pathology associated with them.
口腔癌是可以预防的。主要策略基于预防。大多数口腔癌患者在医院就诊时已经处于晚期,治愈的机会很低。这种情况可能与医生难以做出早期诊断有关。随着信息技术的进步,人工智能(AI)在辅助诊断方面具有很大的潜力。迄今为止,已经开发了一些机器学习算法用于此目的。在本文中,我们将讨论使用 AI 作为工具诊断口腔癌的可能性,以及对人群的影响。我们对一组口腔病变的摄影图像进行了分割,不仅指示了病变区域,还指示了与之相关的病变类别。我们使用不同的神经网络架构进行训练,目的是进行精细分割(逐像素)、对图像裁剪进行分类,以及根据是否存在病变对整个图像进行分类。结果的准确性是可以接受的,不仅为识别病变开辟了可能性,也为分类相关病理开辟了可能性。