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

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
PMID: 38614872

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.

Authors

  • Yun He
    Metanotitia Inc., Shenzhen, China.
  • Yixuan Ji
    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.
  • Shihao Li
    Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
  • Yu Shen
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) 30 South Puzhu Road Nanjing 211816 P. R. China.
  • Lu Ye
    Department of Medical Oncology of Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
  • Ziyan Li
    Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital.C.T.), Chengdu, Sichuan, China.
  • Wenting Huang
    Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital.C.T.), Chengdu, Sichuan, China.
  • Qilian Du
    Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (Hospital.C.T.), Chengdu, Sichuan, China. Electronic address: cheallian@163.com.

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