■下颌第三磨牙容易发生嵌塞,导致其无法爆发进入口腔。需要进行X线检查以支持受累牙齿的牙齿切除术。随着人工智能(AI)技术的进步,基于深度学习的计算机辅助诊断在医学和牙科领域正在兴起。这篇综述描述了深度学习在检测中的性能和前景,分类,并在全景X光片上评估第三磨牙-下颌管关系。
■这项工作是使用三个数据库进行的:PubMed,谷歌学者,科学直接。在文献选择之后,回顾了49篇文章,本文讨论了12篇主要文章。
■目前,在有或没有其他技术组合的情况下,深度学习的几种模型用于第三磨牙嵌塞的分割和分类。深度学习在全景射线照片上识别下颌阻生第三磨牙(ITM)方面表现出显著的诊断性能,精度范围为78.91%至90.23%。同时,深度学习在确定ITM与下颌管(MC)之间关系的准确性范围为72.32%至99%。
■基于深度学习的AI,具有高性能的检测,分类,在过去的十年中,使用全景射线照片对ITM与MC的关系进行了评估。然而,深度学习必须使用大型数据集进行改进,深度学习模型的诊断性能评估应与医学诊断测试协议保持一致。未来的研究涉及口腔放射科医师之间的合作,临床医生,计算机科学家需要确定正确的人工智能开发模型,高效,并适用于临床服务。
UNASSIGNED: Mandibular third molar is prone to impaction, resulting in its inability to erupt into the oral cavity. The radiographic examination is required to support the odontectomy of
impacted teeth. The use of computer-aided diagnosis based on deep learning is emerging in the field of medical and dentistry with the advancement of artificial intelligence (AI) technology. This
review describes the performance and prospects of deep learning for the detection, classification, and evaluation of third molar-mandibular canal relationships on panoramic radiographs.
UNASSIGNED: This work was conducted using three databases: PubMed, Google Scholar, and Science Direct. Following the literature selection, 49 articles were reviewed, with the 12 main articles discussed in this
review.
UNASSIGNED: Several models of deep learning are currently used for segmentation and classification of third molar impaction with or without the combination of other techniques. Deep learning has demonstrated significant diagnostic performance in identifying mandibular
impacted third molars (ITM) on panoramic radiographs, with an accuracy range of 78.91% to 90.23%. Meanwhile, the accuracy of deep learning in determining the relationship between ITM and the mandibular canal (MC) ranges from 72.32% to 99%.
UNASSIGNED: Deep learning-based AI with high performance for the detection, classification, and evaluation of the relationship of ITM to the MC using panoramic radiographs has been developed over the past decade. However, deep learning must be improved using large datasets, and the evaluation of diagnostic performance for deep learning models should be aligned with medical diagnostic test protocols. Future studies involving collaboration among oral radiologists, clinicians, and computer scientists are required to identify appropriate AI development models that are accurate, efficient, and applicable to clinical services.