Mourad Louloua, Aboelsaad Nayer, Talaat Wael M, Fahmy Nada M H, Abdelrahman Hams H, El-Mahallawy Yehia
Oral Surgical Sciences Department, Faculty of Dentistry, Beirut Arab University, Beirut, Lebanon.
Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, UAE; Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, UAE; Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Suez Canal University, Ismailia, Egypt; Chair, Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, UAE.
J Stomatol Oral Maxillofac Surg. 2024 Oct 31;126(4):102124. doi: 10.1016/j.jormas.2024.102124.
The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist.
The diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation.
The differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (P=0.049*). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (p<0.001). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst.
AI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.
本研究旨在参照经验丰富的放射科医生，探讨一种神经网络人工智能模型对颞下颌关节骨关节炎进行影像学确诊的诊断性能。
在一项诊断准确性队列研究中，评估了人工智能模型在识别颞下颌关节骨关节炎患者影像学特征方面的诊断性能。纳入了通过颞下颌关节紊乱病诊断标准决策树选择进行影像学检查的成年患者。通过目标检测YOLO深度学习模型对锥束计算机断层扫描图像进行评估。对照检查者的影像学评估来验证诊断性能。
除皮质下囊肿外（P = 0.049*），人工智能模型与检查者之间的差异在统计学上无显著性。与检查者数据相比，人工智能模型显示出高度一致至近乎完美的一致水平。对于每种影像学表型，人工智能模型具有良好的敏感性、特异性、准确性，并且在统计学上具有高度显著性的受试者工作特征（ROC）分析（p < 0.001）。曲线下面积范围从表面侵蚀的0.872到皮质下囊肿的0.911。
人工智能目标检测模型可为颞下颌关节骨关节炎影像学确诊和具有显著诊断能力的放射组学特征识别提供一种有效、自动化且便捷的模式。
