骨关节炎
颞下颌关节
射线照相术
医学
研究诊断标准
卡帕
医学诊断
口腔正畸科
放射科
髁突
人工智能
计算机科学
病理
数学
地塞米松
替代医学
几何学
作者
Wael Talaat,Shishir Ram Shetty,Saad Al Bayatti,Sameh Talaat,Leona Mourad,Sunaina Shetty,Ahmed Kaboudan
标识
DOI:10.1038/s41598-023-43277-6
摘要
The interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of an artificial intelligence (AI) model for the diagnosis of TMJ osteoarthritis from CBCT. A total of 2737 CBCT images from 943 patients were used for the training and validation of the AI model. The model was based on a single convolutional network while object detection was achieved using a single regression model. Two experienced evaluators performed a Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based assessment to generate a separate model-testing set of 350 images in which the concluded diagnosis was considered the golden reference. The diagnostic performance of the model was then compared to an experienced oral radiologist. The AI diagnosis showed statistically higher agreement with the golden reference compared to the radiologist. Cohen's kappa showed statistically significant differences in the agreement between the AI and the radiologist with the golden reference for the diagnosis of all signs collectively (P = 0.0079) and for subcortical cysts (P = 0.0214). AI is expected to eliminate the subjectivity associated with the human interpretation and expedite the diagnostic process of TMJ osteoarthritis.
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