Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment from Hand Radiograph

Xuhua Ren, Tingting Li, Xiujun Yang, Shuai Wang, Sahar Ahmad, Lei Xiang, Shaun Richard Stone, Lihong Li, Yiqiang Zhan, Dinggang Shen, Qian Wang

Research output: Contribution to journalArticle

6 Citations (Scopus)


Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive and often subject to inter-observer variability. This advocates the need of fully automated method for bone age assessment. We propose a regression convolutional neural network (CNN) to automatically assess the pediatric bone age from hand radiograph. Our network is specifically trained to place more attention to those bone age related regions in the X-ray images. Specifically, we first adopt the attention module to process all images and generate the coarse/fine attention maps as inputs for the regression network. Then, the regression CNN follows the supervision of the dynamic attention loss during training, thus it can estimate the bone age of the hard (or “outlier”) images more accurately. The experimental results show that our method achieves an average discrepancy of 5.2-5.3 months between clinical and automatic bone age evaluations on two large datasets. In conclusion, we propose a fully automated deep learning solution to process X-ray images of the hand for bone age assessment, with the accuracy comparable to human experts but with much better efficiency.

Original languageEnglish
JournalIEEE Journal of Biomedical and Health Informatics
Publication statusAccepted/In press - 2018 Jan 1



  • Bone age assessment
  • Bones
  • Convolutional neural networks
  • deep learning
  • Feature extraction
  • hand radiograph
  • Machine learning
  • Radiography
  • regression convolutional neural network
  • Training
  • X-ray imaging

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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