Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease.
Jonas BianchiJoão Roberto GonçalvesAntônio Carlos de Oliveira RuellasJúlia Vieira Pastana BianchiLawrence M AshmanMarilia YatabeErika BenavidesFabiana Naomi SokiLucia Helena Soares CevidanesPublished in: PloS one (2021)
The objective of this study was to use high-resolution cone-beam computed images (hr- CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen's Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images.
Keyphrases
- high resolution
- end stage renal disease
- deep learning
- chronic kidney disease
- newly diagnosed
- ejection fraction
- mass spectrometry
- cone beam computed tomography
- convolutional neural network
- optical coherence tomography
- prognostic factors
- computed tomography
- artificial intelligence
- patient reported outcomes
- magnetic resonance
- high speed
- tandem mass spectrometry
- liquid chromatography
- photodynamic therapy
- temporal lobe epilepsy
- contrast enhanced