AUTHOR=Mackie Tamara , Al Turkestani Najla , Bianchi Jonas , Li Tengfei , Ruellas Antonio , Gurgel Marcela , Benavides Erika , Soki Fabiana , Cevidanes Lucia TITLE=Quantitative bone imaging biomarkers and joint space analysis of the articular fossa in temporomandibular joint osteoarthritis using artificial intelligence models JOURNAL=Frontiers in Dental Medicine VOLUME=3 YEAR=2022 URL=https://www.frontiersin.org/journals/dental-medicine/articles/10.3389/fdmed.2022.1007011 DOI=10.3389/fdmed.2022.1007011 ISSN=2673-4915 ABSTRACT=
Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for the diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles) and divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (