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

TY - GEN

T1 - An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis

AU - Todnem Bach Christensen, Lena

AU - Straadt, Dikte

AU - Vassis, Stratos

AU - Lillelund, Christian Marius

AU - Stoustrup, Peter Bangsgaard

AU - Pauwels, Ruben

AU - Pedersen, Thomas Klit

AU - Pedersen, Christian Fischer

N1 - Conference code: 46

PY - 2024/12/17

Y1 - 2024/12/17

N2 - 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.

AB - 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.

KW - Computer Science - Machine Learning

KW - Temporomandibular Joints Involvement

KW - Artificial Intelligence

KW - Juvenile Idiopathic Arthritis

KW - Machine Learning

KW - Clinical Decision Support System

UR - http://www.scopus.com/inward/record.url?scp=85214972100&partnerID=8YFLogxK

U2 - 10.1109/EMBC53108.2024.10781771

DO - 10.1109/EMBC53108.2024.10781771

M3 - Article in proceedings

SP - 1

EP - 4

BT - 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA

PB - IEEE

T2 - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Y2 - 15 July 2024 through 19 July 2024

ER -