Deeptoothduo: Multi-Task Age-Sex Estimation and Understanding Via Panoramic Radiograph

Natthanich Hirunchavarod, Pornnakanok Phuphatham, Natnicha Sributsayakarn, Narawit Prathansap, Suchaya Pornprasertsuk-Damrongsri, Varangkanar Jirarattanasopha, Thanapong Intharah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

We proposed DeepToothDuo, a Deep Convolutional Neural Network trained with a multi-task approach to estimate age and sex from a panoramic radiograph. This reduced the number of parameters required when training the model to predict age and sex separately. Moreover, we showed that training model to simultaneously predict age and sex provided better network understanding results from SHAP. Our proposed network could predict sex with 87.38% accuracy and estimate age within 1.96 years error. The model understanding study showed that the network considered anatomical features aligned with existing human dental and anatomical studies.

Original languageEnglish
Title of host publicationIEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798350313338
DOIs
Publication statusPublished - 2024
Event21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Athens, Greece
Duration: 27 May 202430 May 2024

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Country/TerritoryGreece
CityAthens
Period27/05/2430/05/24

Keywords

  • Age Estimation
  • Model Understanding
  • Multi-task Learning
  • Sex Classification
  • SHAP

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