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Artificial intelligence in musculoskeletal ultrasound imaging

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 artif...

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Detalles Bibliográficos
Autores principales: Shin, YiRang, Yang, Jaemoon, Lee, Young Han, Kim, Sungjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Ultrasound in Medicine 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758096/
https://www.ncbi.nlm.nih.gov/pubmed/33242932
http://dx.doi.org/10.14366/usg.20080
Descripción
Sumario: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.