Development of a deep learning model for automated diagnosis of neuromuscular diseases using ultrasound imaging.

ABSTRACT:

Background

Neuromuscular diseases (NMDs) pose significant diagnostic challenges due to their heterogeneous clinical manifestations and the limitations of traditional diagnostic tools. While musculoskeletal ultrasound has become a promising non-invasive modality for evaluating muscle pathology, its diagnostic accuracy remains heavily dependent on the operator's expertise. To address this, we propose a lightweight and interpretable deep learning model to enable automated classification of ultrasound images in NMD screening.

Method

We developed a novel model, termed NMD-AssistNet, which integrates GhostNet as the backbone with CBAM attention modules and depthwise separable convolutions to enhance both efficiency and discriminative capacity. The model was trained and evaluated o

SUBMITTER: Xie J 

PROVIDER: S-EPMC12457126 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature