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January 1, 2026Imaging Science in DentistryOpen Access

Age estimation through mandibular radiomic attributes in panoramic radiographs using convolutional neural networks

Ygor Alexandre Beserra de Sousa Diego Filipe Bezerra Silva Natália Rogério BORELLA Maria Luíza dos Anjos Pontual José Alberto Souza Paulino Patrícia Meira Bento Daniela Pita de Melo

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Key Points

This study aims to develop and validate a convolutional neural network for estimating age from mandibular radiomic attributes in panoramic radiographs.
Cross-sectional study utilizing digital panoramic radiographs of patients aged 2 to 97 years.
600 radiographic images were manually annotated for training a U-Net-based CNN for semantic segmentation.
The age estimation model was based on a CNN architecture with three convolutional blocks for feature extraction.
The model achieved a mean absolute error of 6.91 years and a root mean square error of 9.41 years.
The coefficient of determination (R^2) was 0.799, indicating a strong correlation between chronological and predicted age.
Observations were generally close to the identity line, with increased dispersion noted in older age groups.

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Implication

Cross-sectional study develops an AI model for age estimation using mandibular features, suggesting accuracy with age variability.

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Cite This Study

Sousa et al. (2026) studied this question.

synapsesocial.com/papers/6a2901886f82f25be989dc96 https://doi.org/10.5624/isd.20260079

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