  Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease
during childhood and adolescence. The temporomandibular joints (TMJ) are among
the most frequently affected joints in patients with JIA, and mandibular growth
is especially vulnerable to arthritic changes of the TMJ in children. A
clinical examination is the most cost-effective method to diagnose TMJ
involvement, but clinicians find it difficult to interpret and inaccurate when
used only on clinical examinations. This study implemented an explainable
artificial intelligence (AI) model that can help clinicians assess TMJ
involvement. The classification model was trained using Random Forest on 6154
clinical examinations of 1035 pediatric patients (67% female, 33% male) and
evaluated on its ability to correctly classify TMJ involvement or not on a
separate test set. Most notably, the results show that the model can classify
patients within two years of their first examination as having TMJ involvement
with a precision of 0.86 and a sensitivity of 0.7. The results show promise for
an AI model in the assessment of TMJ involvement in children and as a decision
support tool.
