Muscle ultrasound in hereditary muscle disease

Muscle ultrasound in myopathies

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.

The role of artificial intelligence in electrodiagnostic and neuromuscular medicine: Current state and future directions

The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.