Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images

Ainhoa Vivel-Couso, Nicolás Vila-Blanco, María J. Carreira, Alberto Bugarín-Diz, Inmaculada Tomás +1 more
1/19/2026
cs.CL

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Abstract

Integrating deep learning into healthcare enables personalized care but raises trust issues due to model opacity. To improve transparency, we propose a system for dental age estimation from panoramic images that combines an opaque and a transparent method within a natural language generation (NLG) module. This module produces clinician-friendly textual explanations about the age estimations, designed with dental experts through a rule-based approach. Following the best practices in the field, the quality of the generated explanations was manually validated by dental experts using a questionnaire. The results showed a strong performance, since the experts rated 4.77+/-0.12 (out of 5) on average across the five dimensions considered. We also performed a trustworthy self-assessment procedure following the ALTAI checklist, in which it scored 4.40+/-0.27 (out of 5) across seven dimensions of the AI Trustworthiness Assessment List.

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Cite this paper

@article{vivelcouso2026trustworthy,
  title  = {Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images},
  author = {Ainhoa Vivel-Couso and Nicolás Vila-Blanco and María J. Carreira and Alberto Bugarín-Diz and Inmaculada Tomás and Jose M. Alonso-Moral},
  year   = {2026},
  eprint = {2601.12960},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.12960v1}
}

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