Farajollahi Mehran, Safarian Mohammad Sadegh, Hatami Masoud, Esmaeil Nejad Azadeh, Peters Ove A
Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
School of Dentistry, The University of Queensland, Herston, Queensland, Australia.
Aust Endod J. 2023 Dec;49(3):720-734. doi: 10.1111/aej.12775. Epub 2023 Jul 13.
Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.
骨变化的影像学评估是许多口腔颌面部疾病诊断的主要工具之一。然而,这种评估方法在准确性、一致性和相对较低的诊断效率方面存在局限性。最近,基于人工智能(AI)的算法,如深度学习网络,已被引入作为克服这些挑战的解决方案。根据最近的研究,人工智能可以提高专家临床医生对根尖周病变、牙周疾病及其预后以及种植体周围骨丢失的检测准确性。此外,人工智能已成功用于检测和诊断具有高预测价值的口腔颌面部病变。本研究旨在综述目前关于人工智能在口腔颌面部骨丢失检测和分析中的应用证据。