Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning
Abstract Accurate and interpretable age estimation and gender classification are essential in forensic and clinical diagnostics, particularly when using high-dimensional medical imaging data such as Cone Beam Computed Tomography (CBCT). Traditional CBCT-based approaches often suffer from high comput...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03305-z |
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| author | Najmeh Pishghadam Rasool Esmaeilyfard Maryam Paknahad |
| author_facet | Najmeh Pishghadam Rasool Esmaeilyfard Maryam Paknahad |
| author_sort | Najmeh Pishghadam |
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| description | Abstract Accurate and interpretable age estimation and gender classification are essential in forensic and clinical diagnostics, particularly when using high-dimensional medical imaging data such as Cone Beam Computed Tomography (CBCT). Traditional CBCT-based approaches often suffer from high computational costs and limited interpretability, reducing their applicability in forensic investigations. This study aims to develop a multi-task deep learning framework that enhances both accuracy and explainability in CBCT-based age estimation and gender classification using attention mechanisms. We propose a multi-task learning (MTL) model that simultaneously estimates age and classifies gender using panoramic slices extracted from CBCT scans. To improve interpretability, we integrate Convolutional Block Attention Module (CBAM) and Grad-CAM visualization, highlighting relevant craniofacial regions. The dataset includes 2,426 CBCT images from individuals aged 7 to 23 years, and performance is assessed using Mean Absolute Error (MAE) for age estimation and accuracy for gender classification. The proposed model achieves a MAE of 1.08 years for age estimation and 95.3% accuracy in gender classification, significantly outperforming conventional CBCT-based methods. CBAM enhances the model’s ability to focus on clinically relevant anatomical features, while Grad-CAM provides visual explanations, improving interpretability. Additionally, using panoramic slices instead of full 3D CBCT volumes reduces computational costs without sacrificing accuracy. Our framework improves both accuracy and interpretability in forensic age estimation and gender classification from CBCT images. By incorporating explainable AI techniques, this model provides a computationally efficient and clinically interpretable tool for forensic and medical applications. |
| format | Article |
| id | doaj-art-e4c0d35b36ac49c384434fe4c88b1dd2 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e4c0d35b36ac49c384434fe4c88b1dd22025-08-20T02:29:50ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-03305-zExplainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learningNajmeh Pishghadam0Rasool Esmaeilyfard1Maryam Paknahad2Computer Engineering and Information Technology Department, Shiraz University of TechnologyComputer Engineering and Information Technology Department, Shiraz University of TechnologyOral and Dental Disease Research Center, Oral and Maxillofacial Radiology Department, Dental School, Shiraz University of Medical SciencesAbstract Accurate and interpretable age estimation and gender classification are essential in forensic and clinical diagnostics, particularly when using high-dimensional medical imaging data such as Cone Beam Computed Tomography (CBCT). Traditional CBCT-based approaches often suffer from high computational costs and limited interpretability, reducing their applicability in forensic investigations. This study aims to develop a multi-task deep learning framework that enhances both accuracy and explainability in CBCT-based age estimation and gender classification using attention mechanisms. We propose a multi-task learning (MTL) model that simultaneously estimates age and classifies gender using panoramic slices extracted from CBCT scans. To improve interpretability, we integrate Convolutional Block Attention Module (CBAM) and Grad-CAM visualization, highlighting relevant craniofacial regions. The dataset includes 2,426 CBCT images from individuals aged 7 to 23 years, and performance is assessed using Mean Absolute Error (MAE) for age estimation and accuracy for gender classification. The proposed model achieves a MAE of 1.08 years for age estimation and 95.3% accuracy in gender classification, significantly outperforming conventional CBCT-based methods. CBAM enhances the model’s ability to focus on clinically relevant anatomical features, while Grad-CAM provides visual explanations, improving interpretability. Additionally, using panoramic slices instead of full 3D CBCT volumes reduces computational costs without sacrificing accuracy. Our framework improves both accuracy and interpretability in forensic age estimation and gender classification from CBCT images. By incorporating explainable AI techniques, this model provides a computationally efficient and clinically interpretable tool for forensic and medical applications.https://doi.org/10.1038/s41598-025-03305-zCBCTMulti-task learningAttention mechanismsAge EstimationGender classificationExplainable AI (XAI) |
| spellingShingle | Najmeh Pishghadam Rasool Esmaeilyfard Maryam Paknahad Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning Scientific Reports CBCT Multi-task learning Attention mechanisms Age Estimation Gender classification Explainable AI (XAI) |
| title | Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning |
| title_full | Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning |
| title_fullStr | Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning |
| title_full_unstemmed | Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning |
| title_short | Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning |
| title_sort | explainable deep learning for age and gender estimation in dental cbct scans using attention mechanisms and multi task learning |
| topic | CBCT Multi-task learning Attention mechanisms Age Estimation Gender classification Explainable AI (XAI) |
| url | https://doi.org/10.1038/s41598-025-03305-z |
| work_keys_str_mv | AT najmehpishghadam explainabledeeplearningforageandgenderestimationindentalcbctscansusingattentionmechanismsandmultitasklearning AT rasoolesmaeilyfard explainabledeeplearningforageandgenderestimationindentalcbctscansusingattentionmechanismsandmultitasklearning AT maryampaknahad explainabledeeplearningforageandgenderestimationindentalcbctscansusingattentionmechanismsandmultitasklearning |