An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis

TL;DR

This study presents an explainable AI model using Random Forest to accurately detect TMJ involvement in children with JIA, aiding clinicians in diagnosis with high precision and sensitivity.

Contribution

The paper introduces a novel, explainable AI approach trained on clinical data to improve TMJ involvement detection in pediatric JIA patients.

Findings

Model achieves 0.86 precision in classifying TMJ involvement

Model has 0.7 sensitivity in early detection within two years

AI shows promise as a decision support tool for clinicians

Abstract

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.