AI-Assisted Evaluation of Dental Anxiety in Children: A Machine Learning Approach

This observational study evaluated whether children\'s dental anxiety could be identified from their speech using artificial intelligence and machine learning methods. Children aged 8-12 years attending a pediatric dentistry clinic answered a set of short, standardized questions before receiving dental treatment. Their speech was recorded, and their dental anxiety was assessed during the same session using three established measures: the Children\'s Fear Survey Schedule-Dental Subscale, the Modified Child Dental Anxiety Scale, and the Face Image Scale. Acoustic characteristics of the children\'s voices and linguistic characteristics of their spoken responses were analyzed together. Four machine learning algorithms were developed and evaluated to determine how accurately they could distinguish between children with lower and higher levels of dental anxiety. No treatment was assigned or modified as part of the study.