This study developed an explainable artificial intelligence (AI) model to assess temporomandibular joint (TMJ) involvement in children under 16 years old who suffer from juvenile idiopathic arthritis (JIA). The model was trained using a Random Forest algorithm on a dataset of 6,154 longitudinal clinical examinations of 1,035 pediatric patients.
The key findings are:
Using a "Temporal Segmentation" strategy, 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. This is a significant improvement over previous attempts using only clinical examinations.
The model's explainability, obtained through Shapley Additive exPlanations (SHAP), identifies the most important clinical features for predicting TMJ involvement, such as lateral translation, protrusion, and asymmetry of the jaw.
Incorporating historical clinical data through a "Lagged Features" strategy slightly improves the model's sensitivity by 2.5%, from 0.8 to 0.82, compared to using only the current examination data.
The results demonstrate the potential of an AI-based decision support tool to enhance timely diagnosis of TMJ involvement in children with JIA, which can lead to improved treatment planning and outcomes. The explainable nature of the model also helps clinicians understand and trust the AI's predictions.
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by Lena Todnem ... at arxiv.org 05-06-2024
https://arxiv.org/pdf/2405.01617.pdfDeeper Inquiries