Oliva Vanesa,  Mueller Emily,  Mueller Thomas,  Tolend Mirkamal,  Doria Andrea

Final Pr. ID: Poster #: SCI-006

Temporomandibular joint (TMJ) is involved in approximately 40% of children with juvenile idiopathic arthritis (JIA), but its involvement can be silent, without obvious clinical findings. Contrast-enhanced MRI is therefore key for diagnosis and follow-up of patients, remaining the diagnostic gold standard, as it is able to evaluate both inflammatory and osteochondral changes. Improving the quantitative evaluation of mandibular condylar morphology and radiomic features on MRI has immense practical utility for clinical management decisions in JIA. In this study we evaluated the potential of automated machine learning-based segmentation of the condylar head when applied to routinely acquired TMJ MRI exams. Read More

Authors:  Oliva Vanesa , Mueller Emily , Mueller Thomas , Tolend Mirkamal , Doria Andrea

Keywords:  TMJ