van Alfen N, Gijsbertse K, de Korte CL. How useful is muscle ultrasound in the diagnostic workup of neuromuscular diseases? Curr Opin Neurol. 2018 Oct;31(5):568-574.
Purpose of review This review focuses on developments in muscle ultrasound as a noninvasive and accurate tool for the diagnosis and follow-up of neuromuscular disease. It discusses current muscle ultrasound applications with already proven clinical value, and highlights recent technical developments that may further advance muscle ultrasounds’ diagnostic qualities.
Recent findings The sensitivity and specificity of muscle ultrasound for detecting a neuromuscular disorder are high (90–95%), and quantitative ultrasound is well suited to monitor disease progression in several disorders. Adding ultrasound to electromyography significantly improves diagnostic certainty in patients with suspected motor neuron disease, and ultrasound increases the detection of fasciculations with 30–50%. New developments include speckle tracking of tissue motion to quantify diaphragm excursions and diminished muscle contractility in dystrophy, and strain elastography to detect changes in muscle stiffness and anisotropy during contraction and in disease states. Deep learning algorithms are being developed to predict the presence of a muscle disease and differentiate between disorders.
Summary Muscle ultrasound is excellent for screening, diagnosing, and follow-up of neuromuscular disease. New developments are underway to automate and objectify the diagnostic process, and to quantify tissue motion that can provide new insights in pathophysiology and serve as a biomarker.
From the article:
Muscle ultrasound is a valuable and clinically proven imaging technique for the diagnosis of neuromuscular disorders and needle guidance during invasive diagnostic procedures. QMUS [quantitative ultrasound imaging of muscles] is the most sensitive technique, but it is currently very software- and hardware dependent, which hampers widespread use. Visual evaluation augmented with dynamic imaging can already save patients from more invasive procedures. New techniques such as strain imaging, dedicated QMUS machines without postprocessing, and deep learning systems are promising developments to overcome current limitations and further optimize the diagnostic use of muscle ultrasound.