Musculoskeletal imaging: current and future trends

Douis, Hassan; James, Steven; Davies, A. M.

doi:10.1007/s00330-010-2024-z

Cited by 5 publications

(3 citation statements)

References 21 publications

(23 reference statements)

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“…This year's RadioGraphics monograph focuses on the wide spectrum of musculoskeletal imaging, ranging from essential concepts for targeted joints (upper extremity, pelvis and lower extremity) to multisystem and general pathologic conditions. Musculoskeletal radiology continues to evolve rapidly, benefiting from such advances as continually improving magnets, sequences, and coils in MR imaging as well as broader ap-plication of ultrasonography (US) to the musculoskeletal system (1,2). Likewise, our referring colleagues continue to advance our understanding of biomechanical principles that affect the musculoskeletal system and associated pathologic conditions.…”

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confidence: 99%

The 2016RadioGraphicsMonograph Issue: Musculoskeletal Imaging

Davis¹,

Bancroft²

2016

RadioGraphics

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confidence: 99%

The 2016RadioGraphicsMonograph Issue: Musculoskeletal Imaging

Davis¹,

Bancroft²

2016

RadioGraphics

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“…Additionally, although US has been explored for musculoskeletal imaging, the curation of US musculoskeletal data remains challenging. Concurrently, the identification of the sonographic appearance of specific anatomical structures remains suboptimal and is still evolving [7], and relevant datasets of musculoskeletal US images with expert annotations are limited.…”

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confidence: 99%

Artificial intelligence in musculoskeletal ultrasound imaging

et al. 2021

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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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“…Therefore, MSK Radiology is now emerging as a separate sub-specialty to attain this objective. 1 The present era of rapid innovations in MSK radiologyis marked by the integration of diverse disciplines and technologies that arefurther narrowingthe gap between machine and man. Artificial intelligence and Radiomics allow extraction of a large number of quantitative features from medical images using specific algorithms which is likely to make image interpretation more efficient and accurate.Advancements inbioengineering and life sciences are creating newer treatment options in musculoskeletal disease in the form of image-guided therapy and targeted radionuclide therapy.…”

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confidence: 99%