IADR Abstract Archives
Integrating Clinical and Imaging Markers of Temporomandibular Joint Arthritis
Objectives: To investigate a new method to integrate 3D variations of condylar morphology, corrected multiplanar (MPR) cone-beam computed tomography (CBCT) image interpretation and clinical signs and symptoms of temporomandibular joint osteoarthritis (TMJ OA).
Methods: CBCT scans were obtained for 68 patients with chronic TMJ OA and 33 asymptomatic control subjects. All asymptomatic subjects had radiographic diagnosis of no condylar pathology; TMJ OA patients underwent a clinical exam by an orofacial pain specialist to confirm the TMD research diagnostic criteria. All participants had a CBCT scan taken (i-CAT Next Generation, 120 kV, 18.66 mA). Our 3D analysis approach utilized principal nested spheres (PNS) to obtain information about TMJ OA phenotypes. 3D models of the TMJs were generated from CBCT, and left joints were mirrored into the right side. Correspondent Point distribution models (PDM) were constructed using SPHARM-PDM. TMJ OA joints were subdivided into 3 subgroups (mild, moderate and severe) based on morphological variability determined by PNS and by consensus radiological MPR interpretation from 2 radiologists.
Results: The polychoric correlation coefficient investigated the correlation among image variables, demographic information, and clinical markers. Assuming that each two variables are related to continuous random vector, following bivariate normal distribution, based on the latent variable model, we calculated the polychoric correlation coefficients for any two image marker variables. For age and clinical markers, such as years of onset of symptoms, a cut-off technique transferred the continuous variable to dichotomous variables allowing assessment of both clinical and imaging findings.
Conclusions: These preliminary studies demonstrate that TMJ OA diagnosis derived from diverse image markers as well as clinical information can be integrated considering the polychoric correlation coefficients among these markers. Supported by NIDCR and NIBIB R01DE024450
Methods: CBCT scans were obtained for 68 patients with chronic TMJ OA and 33 asymptomatic control subjects. All asymptomatic subjects had radiographic diagnosis of no condylar pathology; TMJ OA patients underwent a clinical exam by an orofacial pain specialist to confirm the TMD research diagnostic criteria. All participants had a CBCT scan taken (i-CAT Next Generation, 120 kV, 18.66 mA). Our 3D analysis approach utilized principal nested spheres (PNS) to obtain information about TMJ OA phenotypes. 3D models of the TMJs were generated from CBCT, and left joints were mirrored into the right side. Correspondent Point distribution models (PDM) were constructed using SPHARM-PDM. TMJ OA joints were subdivided into 3 subgroups (mild, moderate and severe) based on morphological variability determined by PNS and by consensus radiological MPR interpretation from 2 radiologists.
Results: The polychoric correlation coefficient investigated the correlation among image variables, demographic information, and clinical markers. Assuming that each two variables are related to continuous random vector, following bivariate normal distribution, based on the latent variable model, we calculated the polychoric correlation coefficients for any two image marker variables. For age and clinical markers, such as years of onset of symptoms, a cut-off technique transferred the continuous variable to dichotomous variables allowing assessment of both clinical and imaging findings.
Conclusions: These preliminary studies demonstrate that TMJ OA diagnosis derived from diverse image markers as well as clinical information can be integrated considering the polychoric correlation coefficients among these markers. Supported by NIDCR and NIBIB R01DE024450