Muscle ultrasound as a promising tool: clinical applications and the emerging role of deep learning
10.14253/acn.25009
- Author:
Joo Hye SUNG
- Publication Type:Review Article
- From:
Annals of Clinical Neurophysiology
2026;28(1):22-32
- CountryRepublic of Korea
- Language:English
-
Abstract:
Ultrasound (US) is a patient-friendly imaging modality that is well suited for structural assessments of skeletal muscle and surrounding tissues, and its use for both diagnostic and monitoring in neuromuscular medicine is steadily expanding. Despite its advantages, the broader clinical implementation of muscle US is still hinder by the obtained images and their interpretation varying between operators. Recent advances in artificial intelligence (AI)-particularly in deep learning (DL)-have led to significant innovations in medical imaging. These developments hold considerable promise for addressing the inherent limitations of muscle US and for improving its clinical applicability. This review summarizes key muscle US biomarkers, encompassing both qualitative visual assessments and quantitative parameters. We discuss their utility in the diagnosis of neuromuscular disorders and their potential role as responsive biomarkers for monitoring disease progression, based on findings from previous studies. We also introduce foundational concepts of AI and DL and review recent studies that have applied these technologies in muscle US for the automated segmentation of muscle boundaries, feature extraction, and disease classification. Finally, we highlight current challenges and outline future directions needed to fully realize the potential of AI-enhanced muscle US as a standardized and widely applicable tool for assessing neuromuscular diseases.
