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Background <p>The potential influence of the initial child–dentist interaction on dental anxiety (DA) in preschool children remains insufficiently understood. Accurate assessment of DA using pictorial measures is essential to better characterize this relationship. However, pictorial self-report measures still have several limitations from a contemporary perspective, and evidence regarding the impact of the initial interaction on DA remains limited. This study aimed to evaluate the effect of the initial child–dentist interaction on DA and to explore potential Artificial Intelligence (AI)-based refinements in traditional pictorial measures.</p> Methods <p>An observational pre–post (within-subject repeated-measures) study included 171 children aged 3–6 years attending their first dental visit. An AI-assisted pictorial measure (AI-PM) was designed through an expert panel process addressing the limitations of existing tools. DA was assessed pre and post the initial child–dentist interaction using established pictorial measures and the new tool. Validity, reliability (intraclass correlation coefficient), and inter-measurement agreement (Bland–Altman analysis) were evaluated. Changes in anxiety levels were analyzed using the Wilcoxon signed-rank test (<i>p</i> &lt; 0.05).</p> Results <p>Anxiety scores decreased significantly post initial child–dentist interaction (<i>p</i> &lt; 0.001). The AI-PM demonstrated good-to-excellent validity and reliability, with strong correlations with established pictorial measures (Spearman <i>r</i> = 0.72–0.96, <i>p</i> &lt; 0.001). The reliability analysis using the intraclass correlation coefficient demonstrated good-to-excellent agreement between the AI-based assessment and the traditional measures. Agreement with traditional measures was good-to-excellent, with no evidence of proportional bias in Bland–Altman analyses (<i>p</i> &gt; 0.05).</p> Conclusions <p>The findings suggest that the initial child–dentist interaction may play an important role in children’s DA and that the AI-PM may provide a basis for further refinement of traditional pictorial measures used to assess DA in early childhood.</p> Clinical trial registration <p>The study has been retrospectively registered in ClinicalTrials.gov (Identifier: NCT07387055).</p>

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An AI-Assisted pictorial measure of children’s dental anxiety during the initial child–dentist interaction

  • 期刊论文

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  • 发表日期：

    2026年7月14日

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  • Gülşah Sekmenli Özbek,
  • Sümeyra Akkoç

摘要

Background

The potential influence of the initial child–dentist interaction on dental anxiety (DA) in preschool children remains insufficiently understood. Accurate assessment of DA using pictorial measures is essential to better characterize this relationship. However, pictorial self-report measures still have several limitations from a contemporary perspective, and evidence regarding the impact of the initial interaction on DA remains limited. This study aimed to evaluate the effect of the initial child–dentist interaction on DA and to explore potential Artificial Intelligence (AI)-based refinements in traditional pictorial measures.

Methods

An observational pre–post (within-subject repeated-measures) study included 171 children aged 3–6 years attending their first dental visit. An AI-assisted pictorial measure (AI-PM) was designed through an expert panel process addressing the limitations of existing tools. DA was assessed pre and post the initial child–dentist interaction using established pictorial measures and the new tool. Validity, reliability (intraclass correlation coefficient), and inter-measurement agreement (Bland–Altman analysis) were evaluated. Changes in anxiety levels were analyzed using the Wilcoxon signed-rank test (p < 0.05).

Results

Anxiety scores decreased significantly post initial child–dentist interaction (p < 0.001). The AI-PM demonstrated good-to-excellent validity and reliability, with strong correlations with established pictorial measures (Spearman r = 0.72–0.96, p < 0.001). The reliability analysis using the intraclass correlation coefficient demonstrated good-to-excellent agreement between the AI-based assessment and the traditional measures. Agreement with traditional measures was good-to-excellent, with no evidence of proportional bias in Bland–Altman analyses (p > 0.05).

Conclusions

The findings suggest that the initial child–dentist interaction may play an important role in children’s DA and that the AI-PM may provide a basis for further refinement of traditional pictorial measures used to assess DA in early childhood.

Clinical trial registration

The study has been retrospectively registered in ClinicalTrials.gov (Identifier: NCT07387055).