Age and sex estimation in cephalometric radiographs based on multitask convolutional neural networks

Yun He; Yixuan Ji; Shihao Li; Yu Shen; Lu Ye; Ziyan Li; Wenting Huang; Qilian Du

doi:10.1016/j.oooo.2024.02.010

Age and sex estimation in cephalometric radiographs based on multitask convolutional neural networks

Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Feb 20:S2212-4403(24)00069-5. doi: 10.1016/j.oooo.2024.02.010. Online ahead of print.

Authors

Yun He¹, Yixuan Ji², Shihao Li³, Yu Shen¹, Lu Ye¹, Ziyan Li⁴, Wenting Huang⁴, Qilian Du⁵

Affiliations

¹ College of Preclinical Medicine of Chengdu University, Chengdu, Sichuan, China.
² 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.
³ Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
⁴ Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital.C.T.), Chengdu, Sichuan, China.
⁵ Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital.C.T.), Chengdu, Sichuan, China. Electronic address: cheallian@163.com.

PMID: 38614872
DOI: 10.1016/j.oooo.2024.02.010

Abstract

Objectives: 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.

Study design: 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.

Results: 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.

Conclusions: 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.