Bone age assessment from articular surface and epiphysis using deep neural networks.

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  • Additional Information
    • Source:
      Publisher: American Institute of Mathematical Sciences;; _b Beihang University Country of Publication: United States NLM ID: 101197794 Publication Model: Print Cited Medium: Internet ISSN: 1551-0018 (Electronic) Linking ISSN: 15471063 NLM ISO Abbreviation: Math Biosci Eng Subsets: MEDLINE
    • Publication Information:
      Original Publication: Springfield, MO, USA : [S.l.] : American Institute of Mathematical Sciences; Beihang University
    • Subject Terms:
      Neural Networks, Computer* ; Epiphyses*/diagnostic imaging; Child ; Humans ; Radiography
    • Abstract:
      Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.
    • Contributed Indexing:
      Keywords: articular surface; bone age assessment; deep neural networks; epiphysis; hand radiography
    • Publication Date:
      Date Created: 20230728 Date Completed: 20230731 Latest Revision: 20230731
    • Publication Date:
      20240829
    • Accession Number:
      10.3934/mbe.2023585
    • Accession Number:
      37501481