Artificial intelligence in musculoskeletal ultrasound imaging

• Published in: Ultrasonography (Seoul, Korea) Vol. 40; no. 1; pp. 30 - 44
• Main Authors: Shin, YiRang, Yang, Jaemoon, Lee, Young Han, Kim, Sungjun
• Format: Journal Article
• Language: English
• Published: Korea (South) Korean Society of Ultrasound in Medicine 01.01.2021; 대한초음파의학회
• Subjects: Algorithms; Artificial intelligence; Automation; Classification; deep learning; Engineering; machine learning; Magnetic resonance imaging; Musculoskeletal diseases; Musculoskeletal system; Neural networks; Radiology; Special Review of Artifical Intelligence (Part 1); Tendons; Ultrasonic imaging; ultrasonography; 방사선과학; Deep learning; Musculoskeletal system; Artificial intelligence; Machine learning; Ultrasonography
• ISSN: 2288-5919; 2288-5943
• DOI: 10.14366/usg.20080

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.