Pishghadam Najmeh, Esmaeilyfard Rasool, Paknahad Maryam
Computer Engineering and Information Technology Department, Shiraz University of Technology, Shiraz, Iran.
Oral and Dental Disease Research Center, Oral and Maxillofacial Radiology Department, Dental School, Shiraz University of Medical Sciences, Shiraz, Iran.
Sci Rep. 2025 May 24;15(1):18070. doi: 10.1038/s41598-025-03305-z.
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.
准确且可解释的年龄估计和性别分类在法医和临床诊断中至关重要，尤其是在使用诸如锥形束计算机断层扫描（CBCT）等高维医学影像数据时。传统的基于CBCT的方法通常存在计算成本高和可解释性有限的问题，这降低了它们在法医调查中的适用性。本研究旨在开发一种多任务深度学习框架，通过注意力机制提高基于CBCT的年龄估计和性别分类的准确性和可解释性。我们提出了一种多任务学习（MTL）模型，该模型使用从CBCT扫描中提取的全景切片同时估计年龄和进行性别分类。为了提高可解释性，我们集成了卷积块注意力模块（CBAM）和Grad-CAM可视化，突出显示相关的颅面部区域。该数据集包括来自7至23岁个体的2426张CBCT图像，并使用年龄估计的平均绝对误差（MAE）和性别分类的准确率来评估性能。所提出的模型在年龄估计方面实现了1.08岁的MAE，在性别分类方面达到了95.3%的准确率，显著优于传统的基于CBCT的方法。CBAM增强了模型关注临床相关解剖特征的能力，而Grad-CAM提供了可视化解释，提高了可解释性。此外，使用全景切片而不是完整的3D CBCT体积在不牺牲准确性的情况下降低了计算成本。我们的框架提高了基于CBCT图像的法医年龄估计和性别分类的准确性和可解释性。通过纳入可解释的人工智能技术，该模型为法医和医学应用提供了一种计算高效且临床可解释的工具。
