College of Preclinical Medicine of Chengdu University, Chengdu, Sichuan, China.
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Other Research Platforms, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Jul;138(1):225-231. doi: 10.1016/j.oooo.2024.02.010. Epub 2024 Feb 20.
Age and sex characteristics are evident in cephalometric radiographs (CRs), yet their accurate estimation remains challenging due to the complexity of these images. This study aimed to harness deep learning to automate age and sex estimation from CRs, potentially simplifying their interpretation.
We compared the performance of 4 deep learning models (SVM, R-net, VGG16-SingleTask, and our proposed VGG16-MultiTask) in estimating age and sex from the testing dataset, utilizing a VGG16-based multitask deep learning model on 4,557 CRs. Gradient-weighted class activation mapping (Grad-CAM) was incorporated to identify sex. Performance was assessed using mean absolute error (MAE), specificity, sensitivity, F1 score, and the area under the curve (AUC) in receiver operating characteristic analysis.
The VGG16-MultiTask model outperformed the others, with the lowest MAE (0.864±1.602) and highest sensitivity (0.85), specificity (0.88), F1 score (0.863), and AUC (0.93), demonstrating superior efficacy and robust performance.
The VGG multitask model demonstrates significant potential in enhancing age and sex estimation from cephalometric analysis, underscoring the role of AI in improving biomedical interpretations.
头影测量 X 光片（CR）中存在明显的年龄和性别特征，但由于这些图像的复杂性，准确估计仍然具有挑战性。本研究旨在利用深度学习技术，从 CR 中自动估计年龄和性别，从而可能简化其解释。
我们比较了 4 种深度学习模型（SVM、R-net、VGG16-SingleTask 和我们提出的 VGG16-MultiTask）在利用基于 VGG16 的多任务深度学习模型对 4557 张 CR 进行年龄和性别估计时的性能。采用梯度加权类激活映射（Grad-CAM）来识别性别。使用平均绝对误差（MAE）、特异性、敏感性、F1 分数和受试者工作特征分析中的曲线下面积（AUC）评估性能。
VGG16-MultiTask 模型表现优于其他模型，具有最低的 MAE（0.864±1.602）和最高的敏感性（0.85）、特异性（0.88）、F1 分数（0.863）和 AUC（0.93），表明其具有更好的疗效和稳健的性能。
VGG 多任务模型在增强头影测量分析中的年龄和性别估计方面具有显著潜力，强调了人工智能在改善生物医学解释方面的作用。
