Forensic age estimation for pelvic X-ray images using deep learning.

Journal: European radiology
Published Date:

Abstract

PURPOSE: To develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model.

Authors

  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Zhizhong Huang
    College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, 610065, China.
  • Xiaoai Dong
    Department of Forensic Pathology, West China School of Preclinical and Forensic Medicine, Sichuan University, No. three, 17 South Renmin Road, Wuhou District, Chengdu City, 610041, Sichuan, People's Republic of China.
  • Weibo Liang
    Department of Forensic Genetics, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
  • Hui Xue
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.
  • Yi Zhang
    Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China.
  • Zhenhua Deng
    Department of Forensic Pathology, West China School of Preclinical and Forensic Medicine, Sichuan University, No. three, 17 South Renmin Road, Wuhou District, Chengdu City, 610041, Sichuan, People's Republic of China. fydzh63@163.com.