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Related Experiment Video

Updated: Jul 3, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Deep learning-based dental age classification from panoramic radiographs: a multi-granularity, multi-model

Esin Akol Görgün1, Ezgi Eroğlu Çakmakoğlu2, Savaş Sağmak3

  • 1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Adıyaman University, Altinsehir Neighborhood, 3005 Street No:13, Adiyaman, 02040, Turkey. esinakol@adiyaman.edu.tr.

BMC Oral Health
|July 2, 2026
PubMed
Summary

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This summary is machine-generated.

The hybrid MaxViT-T deep learning model shows high accuracy in dental age estimation from panoramic radiographs. Explainable AI confirms its focus on key developmental dental landmarks for age assessment.

Area of Science:

  • Artificial Intelligence in Radiology
  • Deep Learning for Medical Imaging
  • Forensic Odontology and Age Estimation

Background:

  • Accurate dental age estimation (DAE) is crucial for forensic and clinical applications.
  • Panoramic radiographs are a common imaging modality for assessing dental development.
  • Evaluating advanced deep learning architectures for DAE is an active research area.

Purpose of the Study:

  • To assess the performance of the hybrid MaxViT-T deep learning model for fine-grained DAE and coarse-grained age-group classification.
  • To utilize panoramic radiographs for DAE in individuals aged 6-18 years.
  • To interpret the model's decision-making using Explainable AI (XAI) techniques.

Main Methods:

  • A dataset of 2,761 panoramic radiographs from patients aged 6-18 years was analyzed.
Keywords:
Deep learningDental age estimationExplainable artificial intelligenceForensic dentistryHybrid modelsMulti-granularity classification

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  • Two tasks were performed: 13-class fine-grained age estimation and 3-group age classification.
  • The MaxViT-T model was compared against ten other architectures using 5-fold cross-validation; XAI methods (Grad-CAM, SHAP) were employed for interpretability.

    • Main Results:

    • MaxViT-T achieved the highest accuracy (0.4980) and lowest MAE (0.71 years) for fine-grained DAE.
    • Tolerance-based accuracy reached 84.9% within 1 year and 96.1% within 2 years.
    • For 3-group classification, MaxViT-T attained 91.13% accuracy and an AUC of 0.9669; XAI revealed focus on tooth development and mandibular structures.

    Conclusions:

    • The hybrid MaxViT-T architecture demonstrated superior accuracy for dental age estimation compared to other models.
    • While not statistically significant, the performance advantage highlights the potential of hybrid architectures.
    • The model's attention patterns align with known odontogenic developmental landmarks, validating its biological plausibility.