Artificial intelligence in musculoskeletal ultrasound imaging
Ultrasonography 2021³â 40±Ç 1È£ p.30 ~ p.44
½ÅÀ̶û(Shin Yi-Rang) - Yonsei University College of Medicine Department of Radiology
¾çÀç¹®(Yang Jae-Moon) - Yonsei University College of Medicine Department of Radiology
ÀÌ¿µÇÑ(Lee Young-Han) - Yonsei University College of Medicine Department of Radiology
±è¼ºÁØ(Kim Sung-Jun) - Yonsei University College of Medicine Department of Radiology
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.
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Ultrasonography, Musculoskeletal system, Artificial intelligence, Machine learning, Deep learning
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À¯È¿¼º°á°ú(Recomendation)
AI-based musculoskeletal imaging has progressed step by step toward enhancing anatomical structure visualization and automating quantitative measurements. Recent studies on AI-based musculoskeletal US have suggested that DL techniques may become next-generation diagnostic tools for monitoring the condition of joints, bones, cartilage, ligaments, and muscles.