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 journalArticlepeer-review

70 Citations (Scopus)


Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and 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 a 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
Article number8500181
Pages (from-to)2030-2038
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Publication statusPublished - 2019 Sep


  • Bone age assessment
  • deep learning
  • hand radiograph
  • regression convolutional neural network

ASJC Scopus subject areas

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


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