Muscle ultrasound is a valuable addition to the neuromuscular toolkit in both the clinic and research settings, with proven value and reliability. However, it is currently not fulfilling its full potential in the diagnostic care of patients with neuromuscular disease. This review highlights the possibilities and pitfalls of muscle ultrasound as a diagnostic tool and biomarker, and discusses challenges to its widespread implementation. We expect that limitations in visual image interpretation, posed by user inexperience, could be overcome with simpler scoring systems and the help of deep‐learning algorithms. In addition, more information should be collected on the relation between specific neuromuscular disorders, disease stages, and expected ultrasound abnormalities, as this will enhance specificity of the technique and enable the use of muscle ultrasound as a biomarker. Quantified muscle ultrasound gives the most sensitive results but is hampered by the need for device‐specific reference values. Efforts in creating dedicated muscle ultrasound systems and artificial intelligence to help with image interpretation are expected to improve usability. Finally, the standard inclusion of muscle and nerve ultrasound in neuromuscular teaching curricula and guidelines will facilitate further implementation in practice. Our hope is that this review will help in unleashing muscle ultrasound's full potential.

Peripheral nerve injury is a potentially debilitating disorder that occurs in an estimated 2–3% of all patients with major trauma, in a similar percentage of medical procedures. The workup of these injuries has traditionally been clinical, combined with electrodiagnostic testing. However, this has limitations, especially in the acute phase of the trauma or lack of any recovery, when it is very important to determine nerve continuity and perform surgical exploration and repair in the case of the complete transection or intraneural fibrosis. Ultrasound can help in those situations. It is a versatile imaging technique with a high sensitivity of 93% for detecting focal nerve lesions. Ultrasound can assess the structural integrity of the nerve, neuroma formation and other surrounding abnormalities of bone or foreign bodies impeding the nerve. In addition, this can help to prevent iatrogenic nerve injury by marking the nerve before the procedure. This narrative review gives an overview of why and how nerve ultrasound can play a role in the detection, management and prevention of peripheral nerve injury.

Introduction/Aims: Diaphragm ultrasound is increasingly used in the diagnosis of diaphragm dysfunction and to guide respiratory management in patients with neuromuscular disorders and those who are critically ill. However, the association between diaphragm ultrasound variables and demographic factors like age, sex, and body mass index (BMI) are understudied. Such relationships are important for correct interpretation of normative values and comparison with selected patients groups. The aim of this study was to determine the associations between diaphragm ultrasound variables and subject characteristics.Methods: B‐mode ultrasound was used to image the diaphragm at the zone of apposition in 83 healthy subjects. Diaphragm thickness at resting end‐expiration (T_end‐exp), diaphragm thickness at maximal end‐inspiration (T_max‐insp), diaphragm thickening ratio (T_max‐insp/T_end‐exp), and diaphragm echogenicity were measured. Multivariate linear regression was used to explore the associations between diaphragm ultrasound variables and subject characteristics.Results: T_end‐exp, T_max‐insp, and thickening ratio do not change with age whereas diaphragm echogenicity increases with age. The thickening ratio had a weak negative association with BMI, while T_end‐exp was positively associated with BMI. Men had a larger T_end‐exp and T_max‐insp than women (T_end‐exp 1.6 ± 0.5 and 1.4 ± 0.3 mm; p = .011, T_max‐insp 3.8 ± 1.0 and 3.2 ± 0.9 mm; p = .004), but similar thickening ratios.Discussion: Diaphragm thickness, thickening, and echogenicity measured with ultrasound are associated with factors such as age, BMI, and sex. Therefore, subject characteristics should be considered when interpreting diaphragm ultrasound measurements. In the absence of normative values, matched control groups are a prerequisite for research and in clinical practice.

Introduction/Aims: Visual and quantitative muscle ultrasound are both valid diagnostic tools in neuromuscular diseases. To optimize muscle ultrasound evaluation and facilitate its use in neuromuscular disease, we examined the correlation between visual and quantitative muscle ultrasound analysis and their pitfalls.Methods: Retrospective data from 994 patients with 13,562 muscle ultrasound images were analyzed. Differences in echogenicity z‐score distribution per Heckmatt grade and corresponding correlation coefficients were calculated.Results: Overall, there was a correlation of 0.60 between the two scoring systems, with a gradual increase in z‐score with increasing Heckmatt grades and vice versa. Patients with a neuromuscular disorder had higher Heckmatt grades (p < 0.001) and z‐scores (median z‐score = 0.30, p < 0.001) than patients without. The highest Heckmatt grades and z‐scores were found in patients with either a dystrophy or inflammatory myopathy (both median Heckmatt grade of 2 and median z score of 0.74 and 1.20, respectively). Discrepant scores were infrequent (<2%), but revealed important pitfalls in both grading systems.Discussion: Visual and quantitative muscle ultrasound are complementary techniques to evaluate neuromuscular disease and have a moderate positive correlation. Importantly, we identified specific pitfalls for visual and quantitative muscle ultrasound and how to overcome them in clinical practice.

The 4-point Heckmatt grading scale can easily be used to analyze muscle ultrasound images. The scale is used in an expanding set of muscles and neuromuscular disorders. This prompted the need for evaluation of the measurement properties of the scale in its current form. In this retrospective observational study we included muscle ultrasound images from patients who were undergoing an ultrasound exam for either clinical or research purposes. The primary outcome of this study was to investigate and improve the measurement properties of the Heckmatt scale using Rasch analysis. We investigated whether observers consistently used the 4 response categories. Data was available of 30.967 muscle ultrasound images from 1783 patients and 43 different individual muscles. In 8 of the 43 muscles, observers had difficulty to discriminate between the response categories, especially in bulbar muscles. After rescoring to a 3-point scale, the response categories were consistently used in all 43 muscles. In conclusion, a 3-point Heckmatt grading scale leads to improved accurate scoring compared to the original 4-point Heckmatt grading scale. Using the 3-point Heckmatt grading scale will not only simplify the use of the scale but also enhance its application in clinical practice and research purposes.