Ramezani Kousar, Tofangchiha Maryam
Department of Oral and Maxillofacial Radiology, Dental Caries Prevention Research Center, Qazvin University of Medical Sciences, Qazvin, Iran.
Radiol Res Pract. 2022 Apr 23;2022:1614838. doi: 10.1155/2022/1614838. eCollection 2022.
Early diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients. Optical coherence tomography (OCT) is an optical imaging modality that has been widely investigated in the field of oncology for identification of cancerous entities. Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. This literature review aimed to highlight the features of precancerous and cancerous oral lesions on OCT images and specify how AI can assist in screening and diagnosis of such pathologies.
口腔癌的早期诊断对于提高患者生存率至关重要。目前用于筛查口腔癌前病变和恶性病变患者的策略不幸地遗漏了大量受累患者。光学相干断层扫描（OCT）是一种光学成像方式，在肿瘤学领域已被广泛研究用于识别癌性实体。由于OCT图像的解读需要专业培训，且OCT图像包含无法通过视觉推断的信息，具有训练算法的人工智能（AI）有能力量化视觉上无法检测到的变化，从而克服了阻碍OCT参与口腔肿瘤病变筛查过程的障碍。这篇文献综述旨在突出OCT图像上口腔癌前病变和癌性病变的特征，并明确AI如何辅助此类病变的筛查和诊断。
