Summary of An Explainable and Conformal Ai Model to Detect Temporomandibular Joint Involvement in Children Suffering From Juvenile Idiopathic Arthritis, by Lena Todnem Bach Christensen et al.
An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis
by Lena Todnem Bach Christensen, Dikte Straadt, Stratos Vassis, Christian Marius Lillelund, Peter Bangsgaard Stoustrup, Ruben Pauwels, Thomas Klit Pedersen, Christian Fischer Pedersen
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel explainable artificial intelligence (AI) model is developed to aid clinicians in assessing temporomandibular joints (TMJ) involvement in pediatric patients with juvenile idiopathic arthritis (JIA). The Random Forest-based classification model is trained on 6154 clinical examinations of 1035 pediatric patients and evaluated for its ability to correctly classify TMJ involvement. Notably, the model achieves a precision of 0.86 and sensitivity of 0.7 in classifying patients with TMJ involvement within two years of their first examination. The results show promise for an AI model as a decision support tool in the assessment of TMJ involvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a special kind of artificial intelligence that can help doctors diagnose a problem called temporomandibular joint (TMJ) involvement in children with juvenile arthritis. This is important because TMJ involvement can affect how well a child’s jaw grows and can be hard to diagnose just by looking at the patient. The AI model was trained on data from over 6,000 clinical examinations of children and was able to correctly identify cases of TMJ involvement most of the time. This could be a useful tool for doctors trying to diagnose this problem. |
Keywords
» Artificial intelligence » Classification » Precision » Random forest
