The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.

Journal: International journal of legal medicine
Published Date: Mar 18, 2025

Abstract

INTRODUCTION: Age estimation, especially in adults, presents substantial challenges in different contexts ranging from forensic to clinical applications. Bone mineral density (BMD), with its distinct age-related variations, has emerged as a critical marker in this domain. This study aims to enhance chronological age estimation accuracy using deep learning (DL) incorporating a multi-modality fusion strategy based on BMD.

Authors

  • Yongjie Cao
    Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
  • Ji Zhang
    Department of Neurology, Xiangya Hospital, Central South University, Jiangxi, Nanchang, 330006, Jiangxi, China.
  • Yonggang Ma
    Department of Medical Imaging, 3201 Hospital of Xi'an Jiaotong University Health Science Center, Hanzhong, Shannxi, People's Republic of China.
  • Suhua Zhang
    Institute of Forensic Science, Fudan University, Shanghai, China.
  • Chengtao Li
    School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi'an 170021, China.
  • Shiquan Liu
    Institute of Forensic Science, Fudan University, Shanghai, China. shiquanliu@fudan.edu.cn.
  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Ping Huang
    Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.

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