Abstract
Ultrasound imaging has emerged as a valuable tool in the diagnosis and management of peripheral nerve disorders. The integration of deep learning with ultrasound technology has shown remarkable potential in enhancing diagnostic accuracy and efficiency. This review focuses on the application of deep learning-based approaches in ultrasound diagnostics for peripheral nerve diseases, with particular emphasis on median nerve segmentation in carpal tunnel syndrome. Structured to provide a clear and thorough understanding of the current landscape and future directions, the paper is organized into several key sections: (1) an overview of the current state of the field, (2) research objectives and notable achievements, (3) challenges in dataset utilization specific to medical imaging, (4) trends in the development of deep learning models tailored for ultrasound analysis, and (5) potential future directions for research and clinical application. This review seeks to bridge the gap between technological advancements and clinical practice, potentially paving the way for more accurate and efficient diagnosis and treatment in the field of peripheral nerve disorders.