
==== Front
Curr Opin Neurol
Curr Opin Neurol
CONEU
Current Opinion in Neurology
1350-7540
1473-6551
Lippincott Williams & Wilkins Hagerstown, MD

39051427
WCO370519
10.1097/WCO.0000000000001306
00016
3
MUSCULAR DISEASE: Edited by Shahram Attarian
Muscle ultrasound in myopathies
Vicino Alex a ∗
Veltsista Dimitra b ∗
van Alfen Nens c
a Nerve-Muscle Unit, Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
b Department of Neurology, University Hospital of Patras, Patras, Greece
c Department of Neurology& Clinical Neurophysiology, Clinical Neuromuscular Imaging Group, Donders Center for Neuroscience, Radboudumc, Nijmegen, The Netherlands
Correspondence to Nens van Alfen, MD, PhD, Department of Neurology and Clinical Neurophysiology, Clinical Neuromuscular Imaging Group, Route 652, Radboud university medical center, PO Box 9101, 6500 HB Nijmegen, The Netherlands. Tel: +31 24 3611111; e-mail: nens.vanalfen@radboudumc.nl
10 2024
25 7 2024
37 5 549557
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

Purpose of review

This review highlights recent developments in the field of muscle ultrasound (MUS) for the diagnosis and follow up of muscle disorders.

Recent findings

The diagnostic screening capacity of quantitative grayscale analysis is still sufficient to assess children suspected of a neuromuscular disorder. A combination of visual and quantitative assessment is advised for optimal interpretation. MUS was more sensitive but less specific than MRI for detecting pathology in limb girdle dystrophies and inflammatory myopathies. New techniques such as shearwave elastography and artificial intelligence algorithms for automated image segmentation show promise but need further development for use in everyday practice.

Muscle ultrasound has high correlations with clinical measures of function in skeletal and respiratory muscles and the orofacial region, in most of the myopathies and dystrophies studied. Over time, imaging changes precede changes in clinical status, making them attractive for biomarker use in trials. In Duchenne muscular dystrophy MUS was also responsive to the effects of steroid treatment.

Summary

Muscle ultrasound is a sensitive technique to diagnose and follow up of skeletal, facial and respiratory muscles in neuromuscular disorders. Its role is both complementary to and partially overlapping with that of MRI.

Keywords

biomarker
dystrophy
muscle ultrasound
myopathy
neuromuscular disorder
OPEN-ACCESSTRUE
==== Body
pmcINTRODUCTION

The use of muscle ultrasound for the diagnosis and follow up of myopathies started in the 1980s with two seminal reports on the use of B-mode scanning to diagnose children in realtime [1,2]. Muscle ultrasound, like MRI, is not one technique but a suite of applications ranging from simple B-mode (i.e. brightness mode) imaging to quantified grayscale analysis, tissue texture assessment, elastography, motion capture with speckle tracking, and artificial intelligence (AI)-augmented analysis techniques [3,4,5▪] (see Figs. 1–4). 

Box 1 no caption available

FIGURE 1 Visual (qualitative) evaluation of muscle ultrasound. Muscle ultrasound of quadriceps (rectus femoris and vastus intermedius) muscle, showing different degrees of muscle abnormalities from left (normal MUS with starry night appearance) to right (severe abnormalities). The four images correspond to grades 1 to 4 on the Heckmatt scale, respectively.

FIGURE 2 Quantitative MUS analysis. Artificial intelligence analysis tools (e.g. PyRadiomics) allows to extract additional features like texture and shape from the original image: Different features can be combined for further analysis and compared to a database to allow machine learning.

FIGURE 3 MUS analysis with artificial intelligence. Grayscale histogram analysis of a chosen ROI (=region of interest) in a normal muscle on the left and dystrophic muscle on the right, showing the mean echogenicity value that allows for quantitative MUS analysis.

FIGURE 4 Shear wave elastography. Example of shear wave elastography ultrasound images. A high intensity pulse produces shear waves which propagate laterally. Tracking of laterally propaged shear waves with low intensity pulses allows to measure shear wave velocity, which is a measure of tissue elasticity.

B-mode ultrasound with visual and/or quantitative (QMUS) evaluation is the most validated ultrasound application for detecting muscle disorders [6]. Visual analysis by an experienced operator has a 75–85% sensitivity, with higher accuracy in children and in classic myopathies (Fig. 1).

Quantitative grayscale analysis (Fig. 2) using the mean echogenicity value or backscatter analysis is more sensitive (90–95%), but harder to implement because of the need for age, sex and BMI-specific reference values or proprietary manufacturer information [4].

New analysis tools using artificial intelligence (Fig. 3) are being explored to automate and increase detection efficiency in muscle ultrasound [5▪].

Dynamic imaging can sensitively assess muscle movement, including fasciculation for which it is currently the most sensitive tool [7]. Muscle shearwave elastography (SWE) (Fig. 4) is a more recent ultrasound application, that explores changes in tissue stiffness in several dystrophies and myotonias.

Muscle imaging techniques are used for diagnostic screening, pattern recognition and follow up. Imaging can guide more invasive tests such as needle electromyography and muscle biopsy and provide context information for genetic variants of unknown significance [8].This review describes recent developments in the field of muscle ultrasound, and provides suggestions for the use of the technique in the neuromuscular clinic and research.

GENERAL ROLE OF MUSCLE ULTRASOUND, AND TECHNIQUE DEVELOPMENT

Diagnostic screening value

Initial diagnostic screening studies of muscle ultrasound (MUS; Figs. 1 and 2) showed a high sensitivity and specificity of ≥90% [9], a finding corroborated by a recent meta review [10]. A prospective study reviewed the value of QMUS screening in the age of genomic sequencing, assuming that classic genetic NMDs would nowadays be more easily identified [11], skewing the referrals for screening towards patients with less obvious pathologies. The QMUS sensitivity (83%) and specificity (78%) were slightly lower than two decades ago, but still sufficient to use QMUS for screening.

Dysphagia assessment

Suprahyoid muscle ultrasound with B-mode and M-mode in neuromuscular and stroke patients [12▪] found significantly different muscle displacement during a standardized swallow between healthy persons and patients with mild or severe dysphagia, with a calculated sensitivity of 93.3%, a77.8% positive predictive value and a 95.6% negative predictive value. Ultrasound constituted an improvement over the routine clinical assessment.

Orofacial muscle ultrasound

A strong correlation of ultrasound with chewing and swallowing quality was found using orofacial muscle ultrasound in healthy people and patients suspected of a neuromuscular disorder (NMD) [13]. Rater experience increased the reliability of visual analysis, and condensing the 4-point Heckmatt grading scale to a 3 points scale resulted in easier applicability of visual analysis in practice.

Effects of physical fitness on adipose tissue and muscle ultrasound

A significant inverse correlation between physical fitness and muscle echogenicity and adipose tissue was found in almost 300 children aged 5–9 years [14], underpinning the association of more contractile muscle tissue with higher physical activity levels. In children with progressive NMD, the physical fitness level could affect the offset and slope of echogenicity changes, which deserves consideration when using imaging to inform muscle status in a trial.

Muscle ultrasound image formation

Attenuation of the ultrasound beam [15] decreases muscle echogenicity in the deeper muscle layers in muscle dystrophy patients [16]. Practically this means that the region of interest for MUS analysis should be limited to the superficial muscle parts in patients with advanced disease [17]. A comparison of almost 14 000 muscle ultrasound images evaluated visually and quantitatively [6] showed that these methods are complementary rather than interchangeable, with a positive correlation of 0.7 in myopathies. Advanced fatty degeneration led to clearly abnormal muscles visually and a decrease rather than further increase in echogenicity, i.e. a parabolic rather than a linear correlation. The authors suggested to always combine visual with quantitative assessment.

Shearwave elastography

SWE is an adjunct to structural muscle imaging, mainly explored in Duchenne muscular dystrophy [3,18,19,20] (Fig. 4), where it was less sensitive to change than echogenicity and sound attenuation measures [21]. SWE values depend critically on the muscle studied and the ultrasound equipment used [22] and need machine specific references for their interpretation. In addition, standardized scan protocols are needed for reliable assessment [23], that also incorporate patient status (at rest, after activity, etc.). Only superficial muscle regions can be assessed. More research focusing on the reliability of SWE will be needed before it can be applied in the clinic.

Artificial intelligence in muscle ultrasound

A narrative review evaluated 12 studies that explored the diagnostic use of AI algorithms [24] (Fig. 3) in neuromuscular disease using ultrasound, MRI, EDx and genetics, and found that their classification was already ≥90% accurate [5▪]. This was corroborated by a study showing 94% accuracy for classifying DMD patients by their ambulatory capacity [25]. AI algorithms need careful construction and assessment, as there is a risk of overfitting their predictive capabilities when trained on a selected set of images and patients, which impairs their use in unselected cohorts. This means that practical AI implementation from research to the clinic will require additional effort across the field to make its use safe and accountable [26].

Muscle ultrasound comparison with other diagnostic techniques

A study comparing MUS with time-matched needle EMG of 800 muscles from NMD patients [27▪▪] found that the techniques showed disparate results more often than not, indicating a complementary rather than a comparable role. Either technique could be used reliably to exclude pathology. Muscle ultrasound performed best in the detection of typical myopathies and was less sensitive for mild neurogenic abnormalities. Two studies comparing muscle ultrasound and MRI in FSHD [28,29] corroborated the tight cross-sectional correlation between clinical data, muscle echogenicity and MRI fatty replacement. A 5-year longitudinal follow up found that normal muscles at baseline more often showed an echogenicity increase (in 17%) than an MR fat fraction increase (in 10%), confirming that ultrasound seems better suited for early disease, while MRI is better at capturing late-stage changes [30].

HEREDITARY MYOPATHIES

Despite the exponential advances in MUS with several hundred peer-reviewed studies published to date, research in hereditary myopathies has mostly focused on dystrophinopathies, followed by FSHD. Literature regarding other rare myopathies is limited to few case reports and small series. Nonetheless, several characteristic US findings and patterns have been reported, such as the selective sparing of triceps muscles in Pompe Disease and ‘central clouding’ of rectus femoris in COL6 related congenital muscular dystrophy [31], but an extensive survey of this topic is beyond the scope of this article that presents recent findings.

Dystrophinopathies

In Duchenne muscular dystrophy (DMD )MUS shows a homogeneous muscle echogenicity increase, typically prior to clinical weakness and before a motor development plateau occurs [31]. Initially, adductor magnus and gastrocnemius are involved [32], with most progression in the rectus femoris in younger boys [33]. QMUS changes in rectus femoris correlated significantly with clinical assessments in ambulant, steroid-naïve patients [34], and in corticosteroid-treated patients where QMUS was found to [35] to be responsive to treatment [36]. Orofacial muscles become abnormal early in dystrophinopathies, with no relation the ambulatory status, and predict mastication and swallowing problems [37▪]. Tongue and temporalis muscle ultrasound shows pseudohypertrophy in DMD and BMD [37▪,38]. A strong correlation between spirometry and diaphragm thickening but a weak correlation with diaphragm thickness was found in 74 DMD patients [39].

Fascioscapulohumeral dystrophy

The typical asymmetric muscle involvement in fascioscapulohumeral dystrophy (FSHD) was confirmed with MUS [40]. Trapezius, rectus femoris and rectus abdominis were most commonly affected, with a heterogenous and multifocal hyperechogenicity pattern [31]. MUS showed abnormalities in 94% of symptomatic patients, with strong correlations with symptom status and echogenicity change over time, confirming its biomarker potential [41▪]. Changes in muscle echointensity changes were more important than changes in clinical assessments over 12 months [42].

Limb-girdle muscular dystrophy

The consistency between MUS and muscle MRI for detecting limb-girdle muscular dystrophy (LGMD) subtypes was 80%, with superior sensitivity and accuracy of MUS and higher specificity using MRI, suggesting they might be used interchangeably [40].

Oculopharyngeal muscular dystrophy

MUS performed bilaterally in 11 orofacial and limb muscles, detected significant echogenicity changes in the temporalis, tongue, and deltoid, and diameter decrease in 7/11 muscles measured (temporalis, masseter, digastric, tongue, deltoid, iliopsoas, and soleus), over a period of 20 months [43].

Congenital muscular dystrophy

An observational QMUS study in11 SELENON (SEPN1)-related congenital myopathy patients showed symmetric involvement, most prominently of the sternocleidomastoid and biceps brachialis, while soleus and temporalis were least affected. Diaphragm US displayed severe atrophy and decreased contraction [44]. MUS in LAMA2-related muscular dystrophy showed symmetric involvement, most severely of the sternocleidomastoid and biceps brachialis, while the soleus was least affected; contrasting earlier MRI work. The authors proposed scanning rectus abdominis, vastus lateralis and gastrocnemius as the most responsive outcome measures [45]. MUS performed inFHL1-related reducing-body myopathy showed heterogeneous and focally increased echogenicity in 11/18 patients, with selective involvement of deeper layers of the biceps and tibialis anterior, corroborated by muscle MRI [46].

Congenital myopathies

A MUS study in 40 RYR1-related malignant hyperthermia patients showed frequent abnormalities, most often muscle hypertrophy and increased echogenicity, corroborating previous reports [47].

Metabolic/mitochondrial myopathies

Mitochondrial myopathy patients usually become symptomatic because of an energy deficiency rather than structural muscle pathology. MUS imaging can be normal in the majority of children in the early stages [48]. In contrast, glycogen storage disorders show prominent MUS abnormalities [49]. MUS was proven useful for newborn screening of infantile onset Pompe disease, and to measure outcome in late onset Pompe disease (LOPD) [50]. Muscle echointensity changes were most pronounced in the thoracic paraspinals, quadriceps, medial gastrocnemius, tibialis anterior and biceps brachii [51]. Echo heterogeneity index, but not the shear modulus, correlated strongly negatively with qualitative motor function measures of the lower limbs, with relative sparing of triceps brachii [52▪]. There was a good correlation between echointensity increase and clinical motor function, and increased echogenicity of the abdominal and trunk muscles combined with sparing of the biceps and flexor digitorum muscles could distinguish LOPD from DM1 [53]. Involvement of the tongue was more pronounced in LOPD compared to other acquired and hereditary myopathies [54].

TOXIC AND IATROGENIC MYOPATHIES

Glucocorticoid-induced myopathy

A literature review concluded that evidence supports the use of MUS as a diagnostic and monitoring tool for glucocorticoid (GC)-induced myopathy [55]. Preliminary studies suggest that increased muscle echointensity is more pronounced in the tibialis anterior, and correlates with treatment duration, while atrophy is more prominent in proximal muscles [56,57▪].

Intensive care unit acquired weakness

Several studies have explored the possible diagnostic use of MUS in intensive care unit-acquired weakness (ICU-AW; critical illness neuromyopathy). Common MUS findings are a decrease in muscle CSA and thickness, increase in muscle echointensity and decrease in pennation angle, most obvious in anterior thigh muscles [58]. Muscle echogenicity changes were found from the first day of ICU admission [59]. Novel methods including SWE, superb microvascular imaging and contrast-enhanced ultrasound in ICU-AW patients versus healthy controls also showed significant qualitative changes [60]. Visual evaluation was found superior to quantitative analysis in predicting ICU-AW, with a sensitivity of 86% and a specificity of 60%, and also for discriminating patients with ICU-AW from those without [61▪]. A higher reduction rate of the rectus femoris pennation angle was the best predictor of ICU-AW, compared to rectus femoris CSA, diaphragm and intercostals muscles thickness measurements [58]. A study of changes in muscle mass during the first week of ICU stay, demonstrated significantly higher mortality rates in patients with muscle enlargement, than those with muscle wasting [62].

MYTONIC MYOPATHIES, CHANNELOPATHIES, IDIOPATHIC INFLAMMATORY MYOPATHIES AND IMMUNE CHECKPOINT MYOSITIS

Idiopathic inflammatory myopathies

Evidence is accumulating for a role of MUS in the diagnosis and follow up of idiopathic inflammatory myopathies (IIM) [63]. However, the literature is hampered by the use of the outdated and imprecise categorization of “polymyositis”, that has been replaced by more precise entities: immune-mediated necrotizing myopathies (IMNM), connective tissue disease overlap syndrome (OS), antisynthetetase syndrome (ASS), and inclusion-body myositis (IBM). Dermatomyositis (DM), and its juvenile form (JDM) have been maintained as separate disorders [64,65]. Several studies have explored MUS to diagnose IIM overall. Increased echogenicity showed a good correlation with muscle atrophy on MRI, and an overall good sensitivity but very low specificity for predicting MRI and histopathologic abnormalities [66]. Muscle SWE velocity was significantly lower in the quadriceps and hamstring muscles in IIM compared to healthy controls [67], a finding confirmed in subsequent studies that also demonstrated higher echogenicities in IIM versus healthy persons [68]. Lower SWE velocity was associated with edema and atrophy on MRI (with variation depending on the examined muscle), without a correlation with disease duration or CK levels [67].

MUS has been used to study the effect of immunomodulatory treatment. Qualitative and Heckmatt-scale abnormalities were decreased after 9 weeks of intravenous immunoglobulin treatment, while quantitative MUS and MRI parameters did not show any changes [69]. In JDM, echogenicity and muscle thickness of the rectus femoris could discriminate between patients and healthy controls, and had a good correlation with functional parameters, MRI abnormalities, and changes in power Doppler before and after exercise [70].

Inclusion body myositis

Quantitative echogenicity and muscle thickness are able to discriminate IBM from other IIM and other neuromuscular diseases [71], a finding confirmed in a recent meta-analysis that showed a pooled sensitivity of 82% and specificity of 98% to discriminate IBM from controls. No difference was found for muscle thickness [72]. In the latest IBM diagnostic criteria, increased echogenicity of the flexor digitorum communis, vastus medialis and lateralis and medial gastrocnemius have been included, along with muscle MRI and anticN1A antibodies, as tools to support the diagnosis [73▪▪].

Respiratory MUS found the diaphragm thickening fraction correlated with muscle strength, disease duration, and the dyspnea score, while muscle strength did not [74]; which could help detect respiratory impairment earlier.

Immune-mediated necrotizing myopathy

MUS in 6 patients with anti-HMGC-related IMNM showed a heterogenous and patchy muscle echogenicity increase that became more diffuse with advanced disease. In the upper limbs MUS revealed a selective involvement of biceps brachii more than the triceps [75].

Immune-checkpoint related inflammatory myopathy

Myositis is the most frequent neurologic complication of immune checkpoint inhibitor (ICI) immunotherapy [76]. Imaging with PET-CT and MRI is useful to detect subclinical involvement and inform the differential diagnosis [77]. Respiratory involvement is a potentially lethal complication of ICI myositis; reduced diaphragm thickness at the end of expiration and absence of thickening with inspiration were reported as MUS markers of respiratory impairment [78].

Skeletal muscle channelopathies

Myotonia is one of the core symptoms of channelopathies, together with transient impairment of voluntary contraction (i.e. periodic paralysis) [79]. Myotonia quantification would be a welcome outcome for treatment trials. Its duration was quantified with B-mode MUS [80] or with SWE for during and after contraction [81], although these finding need reproducing in other patients.

Myotonic dystrophies

Myotonic dystrophy type 1 (DM1) could be distinguished from IBM and other IIM by texture analysis with 76% accuracy. This image postprocessing approach, that characterizes the spatial relationship and echogenicity variation of adjacent pixels, showed less pixel variability in the DM1 patients [82]. Respiratory impairment was studied with diaphragm ultrasound in DM1, and found that diaphragm thickness was lower in DM1 patients than healthy persons, accompanied by a reduced thickening ratio. This thickening ratio was also correlated with more severe clinical impairment on the muscular impairment rating scale [83].

CONCLUSION

Muscle ultrasound for neuromuscular disorders is an active field of research and development. Its role is partially overlapping and also complementary to that of MRI. The current evidence suggests that MUS can play a role as a diagnostic and follow up marker for skeletal, facial and respiratory muscles across the various types of myopathies. New techniques including SWE and AI algorithms to support decision making are currently under development.

Acknowledgements

None.

Financial support and sponsorship

None.

Conflicts of interest

Nens van Alfen is an ultrasound instructor for Sonoskills and performs editorial services for Wiley Publishing; all payment goes to their employer. Alex Vicino and Dimitra Veltsista have none.

REFERENCES AND RECOMMENDED READING

Papers of particular interest, published within the annual period of review, have been highlighted as:

▪ of special interest

▪▪ of outstanding interest

∗ Alex Vicino and Dimitra Veltsista, contributed equally to this work.
==== Refs
REFERENCES

1 Heckmatt JZ Leeman S Dubowitz V . Ultrasound imaging in the diagnosis of muscle disease. J Pediatr 1982; 101 :656–660.7131136
2 Heckmatt JohnZ Dubowitz V Leeman S . Detection of pathological change in dystrophic muscle with B-scan ultrasound imaging. Lancet 1980; 315 :1389–1390.
3 Van Alfen N Gijsbertse K De Korte CL . How useful is muscle ultrasound in the diagnostic workup of neuromuscular diseases? Curr Opin Neurol 2018; 31 :568–574.30028736
4 Wijntjes J van Alfen N . Muscle ultrasound: present state and future opportunities. Muscle Nerve 2021; 63 :455–466.33051891
5▪ Piñeros-Fernández MC . Artificial intelligence applications in the diagnosis of neuromuscular diseases: a narrative review. Cureus 2023; 15 :e48458.37942130
This narrative review provides a first overview of the use of artificial intelligence methods in the diagnosis of neuromuscular disorders, finding that almost all algorithms reached 90% accuracy.

6 Wijntjes J Van Der Hoeven J Saris CGJ . Visual versus quantitative analysis of muscle ultrasound in neuromuscular disease. Muscle Nerve 2022; 66 :253–261.35765226
7 Hobson-Webb LD Simmons Z . Ultrasound in the diagnosis and monitoring of amyotrophic lateral sclerosis: a review. Muscle Nerve 2019; 60 :114–123.30989697
8 Gómez-Andrés D Oulhissane A Quijano-Roy S . Two decades of advances in muscle imaging in children: from pattern recognition of muscle diseases to quantification and machine learning approaches. Neuromusc Disord 2021; 31 :1038–1050.34736625
9 Pillen S Scholten RR Zwarts MJ . Quantitative skeletal muscle ultrasonography in children with suspected neuromuscular disease. Muscle Nerve 2003; 27 :699–705.12766981
10 Rahmani N Mohseni-Bandpei MA Vameghi R . Application of ultrasonography in the assessment of skeletal muscles in children with and without neuromuscular disorders: a systematic review. Ultrasound Med Biol 2015; 41 :2275–2283.26027896
11 Boon AJ Wijntjes J O’Brien TG . Diagnostic accuracy of gray scale muscle ultrasound screening for pediatric neuromuscular disease. Muscle Nerve 2021; 64 :50–58.33651404
12▪ Sung JH Baek S-H Park J-W . Dynamic suprahyoid muscle ultrasound in assessing oropharyngeal dysphagia in neurological disorders. Eur J Phys Rehabil Med 2024; 60 :233–244.38332698
This study looked at static and dynamic ultrasound parameters of the suprahyoid muscles in relation to swallowing difficulties in a sizeable group of patients and healthy volunteers, and found a 95.6% negative predictive value to rule out dysphagia that suggest ultrasound could be an adjunct to the routine clinical evaluation to augment dysphagia assessment.

13 Lagarde MLJ Van Den Engel-Hoek L Geurts ACH . Validity and reliability of visual assessment of orofacial muscle ultrasound images using a modified Heckmatt scale. Muscle Nerve 2023; 68 :176–183.37291967
14 García-Alonso Y García-Hermoso A Alonso-Martínez AM . Associations between physical fitness components with muscle ultrasound parameters in prepuberal children. Int J Obes 2022; 46 :960–968.
15 https://radiologykey.com/ultrasound-interactions-and-attenuation/.
16 Spiliopoulos KC Veltsista D Bara S . Effect of beam attenuation on muscle ultrasound echogenicity measurement in muscular dystrophies. J Clin Ultrasound 2022; 50 :1013–1019.35596693
17 Shklyar I Geisbush TR Mijialovic AS . Quantitative muscle ultrasound in Duchenne muscular dystrophy: a comparison of techniques. Muscle Nerve 2015; 51 :207–213.24862337
18 Lin C-W Tsui P-H Lu C-H . Quantifying lower limb muscle stiffness as ambulation function declines in Duchenne muscular dystrophy with acoustic radiation force impulse shear wave elastography. Ultrasound Med Biol 2021; 47 :2880–2889.34284931
19 Lin C-W Shieh J-Y Tsui P-H . Acoustic radiation force impulse shear wave elastography quantifies upper limb muscle in patients with Duchenne muscular dystrophy. Ultrason Sonochem 2023; 101 :106661.37924615
20 Yu H-K Liu X Pan M . Performance of passive muscle stiffness in diagnosis and assessment of disease progression in Duchenne muscular dystrophy. Ultrasound Med Biol 2022; 48 :414–421.34893358
21 Loureiro BMC De Brito MR Iwabe C . Quantitative ultrasonography reveals skeletal muscle abnormalities in carriers of DMD pathogenic variants. Muscle Nerve 2024; 69 :682–690.38517116
22 Vuorenmaa AS Siitama EMK Mäkelä KS . Inter-operator and inter-device reproducibility of shear wave elastography in healthy muscle tissues. J Applied Clin Med Phys 2022; 23 :e13717.35793227
23 Romano A Staber D Grimm A . Limitations of muscle ultrasound shear wave elastography for clinical routine—positioning and muscle selection. Sensors 2021; 21 :8490.34960581
24 https://www.aepd.es/sites/default/files/2019-09/ai-definition.pdf?utm_medium=E-Mail&utm_source=transaction.
25 Liao A-H Chen J-R Liu S-H . Deep learning of ultrasound imaging for evaluating ambulatory function of individuals with Duchenne muscular dystrophy. Diagnostics 2021; 11 :963.34071811
26 AANEM Committee Professional Practice . Role of artificial intelligence in neuromuscular and electrodiagnostic medicine. Muscle Nerve 2024; 69 :523–526.38488281
27▪▪ Wijntjes J Gerritsen J Doorduin J . Comparison of muscle ultrasound and needle electromyography findings in neuromuscular disorders. Muscle Nerve 2024; 69 :148–156.37877239
This study directly compared ultrasound and needle electromyography findings at the single muscle level in 200+ patients and found that the two techniques are complementary rather than interchangeable, and that muscle ultrasound is less suited to pick up on mild neurogenic abnormalities.

28 Fionda L Vanoli F Di Pasquale A . Comparison of quantitative muscle ultrasound and whole-body muscle MRI in facioscapulohumeral muscular dystrophy type 1 patients. Neurol Sci 2023; 44 :4057–4064.37311950
29 Vincenten SCC Voermans NC Cameron D . The complementary use of muscle ultrasound and MRI in FSHD: Early versus later disease stage follow-up. Clin Neurophysiol 2024; S1388-2457 :00064-6.
30 Janssen BH Pillen S Voet NBM . Quantitative muscle ultrasound versus quantitative magnetic resonance imaging in facioscapulohumeral dystrophy. Muscle Nerve 2014; 50 :968–975.24659533
31 Hannaford A Vucic S Van Alfen N . Muscle ultrasound in hereditary muscle disease. Neuromusc Disord 2022; 32 :851–863.36323605
32 Vill K Sehri M Müller C . Qualitative and quantitative muscle ultrasound in patients with Duchenne muscular dystrophy: where do sonographic changes begin? Eur J Paediatr Neurol 2020; 28 :142–150.32758414
33 Zaidman CM Wu JS Kapur K . Quantitative muscle ultrasound detects disease progression in Duchenne muscular dystrophy. Ann Neurol 2017; 81 :633–640.28241384
34 Abdulhady H Sakr HM Elsayed NS . Ambulatory Duchenne muscular dystrophy children: cross-sectional correlation between function, quantitative muscle ultrasound and MRI. Acta Myol 2022; 41 :1–14.35465338
35 Hu J Jiang L Hong S . Quantitative muscle ultrasound in children with Duchenne muscular dystrophy: Comparing to magnetic resonance imaging. J Clin Ultrasound 2023; 51 :674–679.36449321
36 Hu J Ye Y Kong M . Daily prednisone treatment in Duchenne muscular dystrophy in southwest china. Muscle Nerve 2015; 52 :1001–1007.25809413
37▪ Lagarde MLJ Van Alfen N Geurts ACH . Orofacial muscles may be affected in early stages of Becker muscular dystrophy: a preliminary study. Muscle Nerve 2020; 61 :213–217.31778229
This study showed that BMD patients had earlier involvement of masticatory muscles compared to DMD patients, relatively to their gait ability. Therefore, a higher level of suspicion for dysphagia is warranted in ambulatory BMD patients.

38 Umemoto G Fujioka S Arahata H . Longitudinal changes of tongue thickness and tongue pressure in neuromuscular disorders. BMC Neurol 2021; 21 :302.34353291
39 Fayssoil A Nguyen LS Stojkovic T . Determinants of diaphragm inspiratory motion, diaphragm thickening, and its performance for predicting respiratory restrictive pattern in Duchenne muscular dystrophy. Muscle Nerve 2022; 65 :89–95.34618930
40 Veltsista D Chroni E . Ultrasound pattern of anterolateral leg muscles in facioscapulohumeral muscular dystrophy. Acta Neurol Scand 2021; 144 :216–220.33844852
41▪ Vincenten SCC Teeselink S Voermans NC . Establishing the role of muscle ultrasound as an imaging biomarker in facioscapulohumeral muscular dystrophy. Neuromuscul Disord 2023; 33 :936–944.37968164
This study of a relatively large sample of FSHD patients with a wide spectrum of disease severity, revealed a strong correlation between MUS and clinical outcomes measures confirming its use as a clinical biomarker, and suggesting its potential use as a monitoring biomarker

42 Goselink RJM Schreuder THA Mul K . Muscle ultrasound is a responsive biomarker in facioscapulohumeral dystrophy. Neurology 2020; 94 :e1488–e1494.32132178
43 Kroon RHMJM Kalf JG Meijers RL . Muscle ultrasound is a sensitive biomarker in oculopharyngeal muscular dystrophy. Muscle Nerve 2022; 66 :453–461.35859342
44 Bouman K Groothuis JT Doorduin J . SELENON-related myopathy across the life span, a cross-sectional study for preparing trial readiness. JND 2023; 10 :1055–1074.37807786
45 Bouman K Groothuis JT Doorduin J . LAMA2-related muscular dystrophy across the life span: a cross-sectional study. Neurol Genet 2023; 9 :e200089.37476021
46 Mohassel P Yun P Syeda S . Ann Clin Transl Neurol 2023; 10 :1442–1455.37483011
47 van den Bersselaar LR Heytens L Silva HCA . European Neuromuscular Centre consensus statement on anaesthesia in patients with neuromuscular disorders. Eur J Neurol 2022; 29 :3486–3507.35971866
48 Pillen S Morava E Van Keimpema M . Skeletal muscle ultrasonography in children with a dysfunction in the oxidative phosphorylation system. Neuropediatrics 2006; 37 :142–147.16967365
49 Verbeek RJ Sentner CP Smit GPA . Muscle ultrasound in patients with glycogen storage disease types I and III. Ultrasound Med Biol 2016; 42 :133–142.26437929
50 Hobson-Webb LD Zwelling PJ Raja SS . Quantitative muscle ultrasound and electrical impedance myography in late onset Pompe disease: a pilot study of reliability, longitudinal change and correlation with function. Mol Genet Metab Rep 2021; 28 :100785.34401343
51 Jackson DG Case LE Huggins E . Muscle ultrasound in patients with late-onset Pompe disease identified by newborn screening. Mol Genet Metab Rep 2023; 36 :100989.37670900
52▪ Chiu Y-H Liao C-L Chien Y-H . Sonographic evaluations of the skeletal muscles in patients with Pompe disease. Eur J Paediatr Neurol 2023; 42 :22–27.36508847
In this study, the authors used the Echo heterogeneity index as a quantitative measure of echogenicity, to overcome the known problem of gray-scale analysis in severely affected muscles (parabolic correlation). They found that EH index, but not the shear modulus, negatively correlated with quantitative measures of lower limbs motor function.

53 Hsieh P-C Chang C-W Ro L-S . Ultrasonography of abdominal muscles: Differential diagnosis of late-onset Pompe disease and myotonic dystrophy type 1. Front Neurol 2022; 13 :944464.36147041
54 Jones HN Hobson-Webb LD Kuchibhatla M . Tongue weakness and atrophy differentiates late-onset Pompe disease from other forms of acquired/hereditary myopathy. Mol Genet Metab 2021; 133 :261–268.34053870
55 Wu M Liu C Sun D . Glucocorticoid-Induced myopathy: typology, pathogenesis, diagnosis, and treatment. Horm Metab Res 2024; 56 :341–349.38224966
56 Martucci MG McIlduff CE Shin C . Quantitative ultrasound of muscle can detect corticosteroid effects. Clin Neurophysiol 2019; 130 :1460–1464.31122815
57▪ Minetto MA Caresio C Salvi M . Ultrasound-based detection of glucocorticoid-induced impairments of muscle mass and structure in Cushing's disease. J Endocrinol Invest 2019; 42 :757–768.30443856
In this study the tibialis anterior was shown as one of the three lower limb muscles with maximal increase in echogenicity in active Cushing's disease, supporting an increased susceptibility of tibialis anterior to corticosteroids, despite the lower percentage of fast-twitch muscle fibers compared to the other studied muscles

58 Paolo F Valentina DG Silvia C . The possible predictive value of muscle ultrasound in the diagnosis of ICUAW in long-term critically ill patients. J Crit Care 2022; 71 :154104.35797827
59 Bulinski C Viard M Vlazak A . Neuromuscular sonography detects changes in muscle echotexture and nerve diameter in ICU patients within 24 h. J Ultrasound 2021; 25 :535–545.34870825
60 Hernández-Socorro CR Saavedra P López-Fernández JC . Novel high-quality sonographic methods to diagnose muscle wasting in long-stay critically ill patients: shear wave elastography, superb microvascular imaging and contrast-enhanced ultrasound. Nutrients 2021; 13 :2224.34209526
61▪ Klawitter F Walter U Axer H . Neuromuscular ultrasound in intensive care unit-acquired weakness: current state and future directions. Medicina 2023; 59 :844.37241077
Qualitative MUS evaluation was found superior to quantitative analysis in predicting ICU-AW, (sensitivity 86% and specificity 60%), as well as discriminating between patients with and without ICU-AW.

62 Fuest KE Lanz H Schulz J . Comparison of different ultrasound methods to assess changes in muscle mass in critically ill patients. J Intensive Care Med 2023; 38 :431–439.36227022
63 Albayda J Van Alfen N . Diagnostic value of muscle ultrasound for myopathies and myositis. Curr Rheumatol Rep 2020; 22 :82.32989482
64 Mariampillai K Granger B Amelin D . Development of a new classification system for idiopathic inflammatory myopathies based on clinical manifestations and myositis-specific autoantibodies. JAMA Neurol 2018; 75 :1528.30208379
65 Lundberg IE Tjärnlund A Bottai M . 2017 European League Against Rheumatism/American College of Rheumatology classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups. Ann Rheum Dis 2017; 76 :1955–1964.29079590
66 Paramalingam S Needham M Harris S . Muscle B mode ultrasound and shear-wave elastography in idiopathic inflammatory myopathies (SWIM): criterion validation against MRI and muscle biopsy findings in an incident patient cohort. BMC Rheumatol 2022; 6 :47.35934717
67 Alfuraih AM O’Connor P Tan AL . Muscle shear wave elastography in idiopathic inflammatory myopathies: a case–control study with MRI correlation. Skeletal Radiol 2019; 48 :1209–1219.30810778
68 Li M Guo R Tang X . Quantitative assessment of muscle properties in polymyositis and dermatomyositis using high-frequency ultrasound and shear wave elastography. Quant Imaging Med Surg 2023; 13 :428–440.36620135
69 Walter AW Lim J Raaphorst J . Ultrasound and MR muscle imaging in new onset idiopathic inflammatory myopathies at diagnosis and after treatment: a comparative pilot study. Rheumatology (Oxford) 2023; 62 :300–309.
70 Mamyrova G McBride E Yao L . Preliminary validation of muscle ultrasound in juvenile dermatomyositis (JDM). Rheumatology (Oxford) 2022; 61 :SI48–SI55.34791066
71 Leeuwenberg KE van Alfen N Christopher-Stine L . Ultrasound can differentiate inclusion body myositis from disease mimics. Muscle Nerve 2020; 61 :783–788.32239702
72 Abdelnaby R Mohamed KA Elgenidy A . Muscle sonography in inclusion body myositis: a systematic review and meta-analysis of 944 measurements. Cells 2022; 11 :600.35203250
73▪▪ Lilleker JB Naddaf E Saris CGJ . 272nd ENMC international workshop: 10 Years of progress – revision of the ENMC 2013 diagnostic criteria for inclusion body myositis and clinical trial readiness. 16–18 June 2023, Hoofddorp, The Netherlands. Neuromusc Disord 2024; 37 :36–51.38522330
In the last revision of IBM ENMC's diagnostic criteria, MUS has been included among the supportive complementary exams.

74 Lelièvre MH Hudson M Botez SA . Determinants and functional impacts of diaphragmatic involvement in patients with inclusion body myositis. Muscle Nerve 2021; 63 :497–505.33432607
75 Mohassel P Landon-Cardinal O Foley AR . Anti-HMGCR myopathy may resemble limb-girdle muscular dystrophy. Neurol Neuroimmunol Neuroinflamm 2019; 6 :e523.30588482
76 Marini A Bernardini A Gigli GL . Neurologic adverse events of immune checkpoint inhibitors. Neurology 2021; 96 :754–766.33653902
77 Vicino A Hottinger AF Latifyan S . Immune checkpoint inhibitor-related myositis and myocarditis: diagnostic pitfalls and imaging contribution in a real-world, institutional case series. J Neurol 2023; 271 :1947–1958.38141128
78 Sekiguchi K Hashimoto R Noda Y . Diaphragm involvement in immune checkpoint inhibitor-related myositis. Muscle Nerve 2019; 60 :E23–E25.31323130
79 Vicino A Brugnoni R Maggi L . Diagnostics in skeletal muscle channelopathies. Expert Rev Mol Diagn 2023; 23 :1175–1193.38009256
80 Abraham A Breiner A Barnett C . Quantitative sonographic assessment of myotonia. Muscle Nerve 2018; 57 :146–149.28561926
81 Kronlage C Grimm A Romano A . Muscle ultrasound shear wave elastography as a noninvasive biomarker in Myotonia. Diagnostics 2021; 11 :163.33498617
82 Nodera H Sogawa K Takamatsu N . Texture analysis of sonographic muscle images can distinguish myopathic conditions 2019; 66 :237–247.
83 Henke C Spiesshoefer J Kabitz HJ . Characteristics of respiratory muscle involvement in myotonic dystrophy type 1. Neuromuscul Disord 2020; 30 :17–27.31839403
