Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks

  • Vladimir I. Iglovikov
  • Alexander Rakhlin
  • Alexandr A. KalininEmail author
  • Alexey A. Shvets
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11045)


Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. This dataset consists of 12,600 radiological images. Each radiograph in the dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach introduces a comprehensive preprocessing protocol based on the positive mining technique. We use images of whole hands as well as specific hand parts for both training and prediction. This allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested methods in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.


Medical imaging Computer-aided diagnosis (CAD) Computer vision Image recognition Deep learning 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Vladimir I. Iglovikov
    • 1
  • Alexander Rakhlin
    • 2
  • Alexandr A. Kalinin
    • 3
    Email author
  • Alexey A. Shvets
    • 4
  1. 1.ODS.aiSan FranciscoUSA
  2. 2.Neuromation OUTallinnEstonia
  3. 3.University of MichiganAnn ArborUSA
  4. 4.Massachusetts Institute of TechnologyCambridgeUSA

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