Ultrasonography.  2021 Jan;40(1):30-44. 10.14366/usg.20080.

Artificial intelligence in musculoskeletal ultrasound imaging

Affiliations
  • 1Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
  • 2Systems Molecular Radiology at Yonsei (SysMolRaY), Seoul, Korea
  • 3Severance Biomedical Science Institute (SBSI), Yonsei University College of Medicine, Seoul, Korea

Abstract

Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.

Keyword

Ultrasonography; Musculoskeletal system; Artificial intelligence; Machine learning; Deep learning
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