Update in Musculoskeletal Ultrasound Research

Nwawka, O. Kenechi

doi:10.1177/1941738116664326

Cited by 17 publications

(10 citation statements)

References 109 publications

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“…In contrast, US is less expensive, portable, and capable of bedside imaging. Multiple US-based metrics can also be acquired including brightness-mode and shear wave elastography, which images tissue using soundwaves ( 16 , 17 ).…”

Section: Introductionmentioning

confidence: 99%

Myosteatosis as a Shared Biomarker for Sarcopenia and Cachexia Using MRI and Ultrasound

Lortie¹,

Rush²,

Osterbauer³

et al. 2022

Front. Rehabilit. Sci.

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PurposeEstablish bedside biomarkers of myosteatosis for sarcopenia and cachexia. We compared ultrasound biomarkers against MRI-based percent fat, histology, and CT-based muscle density among healthy adults and adults undergoing treatment for lung cancer.MethodsWe compared ultrasound and MRI myosteatosis measures among young healthy, older healthy, and older adults with non-small cell lung cancer undergoing systemic treatment, all without significant medical concerns, in a cross-sectional pilot study. We assessed each participant's rectus femoris ultrasound-based echo intensity (EI), shear wave elastography-based shear wave speed, and MRI-based proton density fat-fraction (PDFF). We also assessed BMI, rectus femoris thickness and cross-sectional area. Rectus femoris biopsies were taken for all older adults (n = 20) and we analyzed chest CT scans for older adults undergoing treatment (n = 10). We determined associations between muscle assessments and BMI, and compared these assessments between groups.ResultsA total of 10 young healthy adults, 10 older healthy adults, and 10 older adults undergoing treatment were recruited. PDFF was lower in young adults than in older healthy adults and older adults undergoing treatment (0.3 vs. 2.8 vs. 2.9%, respectively, p = 0.01). Young adults had significantly lower EI than older healthy adults, but not older adults undergoing treatment (48.6 vs. 81.8 vs. 75.4, p = 0.02). When comparing associations between measures, PDFF was strongly associated with EI (ρ = 0.75, p < 0.01) and moderately negatively associated with shear wave speed (ρ = −0.49, p < 0.01) but not BMI, whole leg cross-sectional area, or rectus femoris cross-sectional area. Among participants with CT scans, paraspinal muscle density was significantly associated with PDFF (ρ = −0.70, p = 0.023). Histological markers of inflammation or degradation did not differ between older adult groups.ConclusionPDFF was sensitive to myosteatosis between young adults and both older adult groups. EI was less sensitive to myosteatosis between groups, yet EI was strongly associated with PDFF unlike BMI, which is typically used in cachexia diagnosis. Our results suggest that ultrasound measures may serve to determine myosteatosis at the bedside and are more useful diagnostically than traditional weight assessments like BMI. These results show promise of using EI, shear wave speed, and PDFF proxies of myosteatosis as diagnostic and therapeutic biomarkers of sarcopenia and cachexia.

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Section: Introductionmentioning

confidence: 99%

Myosteatosis as a Shared Biomarker for Sarcopenia and Cachexia Using MRI and Ultrasound

Lortie¹,

Rush²,

Osterbauer³

et al. 2022

Front. Rehabilit. Sci.

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“…It is noninvasive and delivers real-time imaging without the use of ionizing radiation. It’ is also popular for evaluating ligaments, muscles, and tendons, as well as superficial cancers and peripheral nerves ( 26 , 30 , 31 ) .…”

Section: Discussionmentioning

confidence: 99%

A deep learning approach for masseter muscle segmentation on ultrasonography

Keser¹,

Bayrakdar²,

Pekiner³

et al. 2022

J Ultrason

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Aim: Deep learning algorithms have lately been used for medical image processing, and they have showed promise in a range of applications. The purpose of this study was to develop and test computer-based diagnostic tools for evaluating masseter muscle segmentation on ultrasonography images. Materials and methods: A total of 388 anonymous adult masseter muscle retrospective ultrasonographic images were evaluated. The masseter muscle was labeled on ultrasonography images using the polygonal type labeling method with the CranioCatch labeling program (CranioCatch, Eskişehir, Turkey). All images were re-checked and verified by Oral and Maxillofacial Radiology experts. This data set was divided into training (n = 312), verification (n = 38) and test (n = 38) sets. In the study, an artificial intelligence model was developed using PyTorch U-Net architecture, which is a deep learning approach. Results: In our study, the artificial intelligence deep learning model known as U-net provided the detection and segmentation of all test images, and when the success rate in the estimation of the images was evaluated, the F1, sensitivity and precision results of the model were 1.0, 1.0 and 1.0, respectively. Conclusion: Artificial intelligence shows promise in automatic segmentation of masseter muscle on ultrasonography images. This strategy can aid surgeons, radiologists, and other medical practitioners in reducing diagnostic time.

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“…It provides real-time imaging without ionizing radiation and is noninvasive. It has also gained popularity for the dynamic imaging of small structures and the evaluation of the ligaments, muscle and tendons, superficial tumors, and peripheral nerves [ 1 ]. Progressive advances in US, including refined transducer technology, power Doppler sonography, and real-time US elastography (EUS), have expanded its clinical applications in the field of musculoskeletal imaging.…”

Section: Introductionmentioning

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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Update in Musculoskeletal Ultrasound Research

Cited by 17 publications

References 109 publications

Myosteatosis as a Shared Biomarker for Sarcopenia and Cachexia Using MRI and Ultrasound

Myosteatosis as a Shared Biomarker for Sarcopenia and Cachexia Using MRI and Ultrasound

A deep learning approach for masseter muscle segmentation on ultrasonography

Artificial intelligence in musculoskeletal ultrasound imaging

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