Related Experiment Video
Updated: Jul 17, 2026
A Morphometric and Cellular Analysis Method for the Murine Mandibular Condyle
Published on: January 11, 2018
Automated diagnosis and classification of temporomandibular joint degenerative disease via artificial intelligence
Wei-Yu Mao1, Yuan-Yuan Fang1, Zhong-Zhen Wang2
1Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, PR China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, PR China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, PR China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, PR China.
Objectives:
In this study, artificial intelligence (AI) techniques were used to achieve automated diagnosis and classification of temporomandibular joint (TMJ) degenerative joint disease (DJD) on cone beam computed tomography (CBCT) images.
Methods:
An AI model utilizing the YOLOv10 algorithm was trained, validated and tested on 7357 annotated and corrected oblique sagittal TMJ images (3010 images of normal condyles and 4347 images of condyles with DJD) from 1018 patients who visited Peking University School and Hospital of Stomatology for temporomandibular disorders and underwent TMJ CBCT examinations. This model could identify DJD as well as the radiographic signs of DJD, namely, erosion, osteophytes, sclerosis and subchondral cysts. The diagnosis and classification performances of the model were evaluated on the test set. The accuracy of the model for evaluating images with one to four DJD signs was also evaluated.
Results:
The accuracy, precision, sensitivity, specificity, F1 score and mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 of the model for DJD detection all exceeded 0.95. The accuracies for identifying erosion, osteophytes, sclerosis and subchondral cysts were 0.91, 0.96, 0.91 and 0.96, respectively. The precisions, specificities and F1 scores for the DJD signs were all >0.90. The sensitivity ranged from 0.88 to 0.95, and the mAP (IoU=0.5) ranged from 0.87 to 0.97. The accuracies of the model for detecting one to four DJD signs in one image were 94 %, 84 %, 66 % and 63 %, respectively.
Conclusions:
A deep learning model based on the YOLOv10 algorithm can not only detect the presence of TMJ DJD on CBCT images but also differentiate the typical radiographic signs of DJD, including erosion, osteophytes, sclerosis and subchondral cysts, with acceptable accuracy.
Clinical Significance:
TMJ DJD is a very common disease that causes joint pain and mandibular dysfunction and affects patients' quality of life; therefore, early diagnosis and intervention are particularly important. However, identifying radiographic signs of early-stage TMJ DJD is difficult. AI can quickly review CBCT images and assist in the accurate and rapid diagnosis and classification of TMJ DJD.
Related Concept Videos
01:10Computed Tomography
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
01:14Imaging Studies I: CT and MRI
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
01:27Imaging Studies III: Computed Tomography