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Volume 45 Issue 1
Jan.  2022
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Article Navigation >  Journal of Molecular Imaging  > 2022  >  45(1): 151-156
LIU Enqing, TAN Xuemei, JIANG Ziyue, HUANG Chengjie, ZHANG Weicong, LÜ Hai, SU Zhihai. Advances in the application of deep learning in orthopedics[J]. Journal of Molecular Imaging, 2022, 45(1): 151-156. doi: 10.12122/j.issn.1674-4500.2022.01.30
Citation: LIU Enqing, TAN Xuemei, JIANG Ziyue, HUANG Chengjie, ZHANG Weicong, LÜ Hai, SU Zhihai. Advances in the application of deep learning in orthopedics[J]. Journal of Molecular Imaging, 2022, 45(1): 151-156. doi: 10.12122/j.issn.1674-4500.2022.01.30
shu

Advances in the application of deep learning in orthopedics

doi: 10.12122/j.issn.1674-4500.2022.01.30
  • 1.

    Department of Spinal Surgery, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510000, China

  • 2.

    Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai 519000, China

  • Received Date: 2021-12-21
    Available Online: 2022-03-29
  • Publish Date: 2022-01-20
    Abstract
  • Deep learning is currently one of the most rapidly developing branches of artificial intelligence. Deep learning can automatically extract good feature expressions in large sample data, effectively improve the performance of various machine learning tasks, which is widely used in image signal processing, computer vision, and natural language processing. With the development of digital imaging, deep learning has become one of the essential techniques for the clinical application of medical image analysis with the advantages of automatically extracting features and efficiently processing high-dimensional medical image data. At present, this technology has reached the level of radiologists in analyzing certain medical images, such as detecting and recognizing lung nodules and classifying knee joint degeneration, which will provide a new chance for the development of computer science in medical applications. Due to the wide variety of diseases in the orthopedics field, clear image data features, and complex and rich contents in the orthopedic field, related learning tasks and application scenarios put forward a new challenge for deep learning. This article reviews the application research progress of deep learning in orthopedics from five clinical image processing and analysis tasks including bone and joint critical parameter measurement, lesion detection, disease grading, image segmentation, and image registration and outlook the development for the reference of orthopedics-related researchers.

     

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