Performance benchmarking of deep learning models for real‐time median nerve segmentation and cross‐sectional area measurement in ultrasound imaging

BACKGROUND: Median nerve, a major peripheral nerve, connects the hand to the central nervous system, facilitating upper limb motor function and sensation by transmitting sensory data from the palm and fingers. Damage to this nerve can result in motor and sensory deficits, with carpal tunnel syndrome (CTS) causing compression, leading to tingling and numbness in the thumb, index, middle, and lateral ring fingers. PURPOSE: This study aimed to develop an accurate deep-learning-based segmentation method for measuring the cross-sectional area (CSA) of the median nerve to facilitate the diagnosis of nerve entrapment syndromes and aid in surgical planning, with a focus on CTS. METHODS: -value threshold of 0.05, while statistical equivalence between models within predefined margins was formally assessed using the two one-sided test (TOST) procedure. Following quantitative validation, the model was deployed in a real-time clinical setup utilizing an Av.io HD Epiphan frame grabber to stream ultrasound images from the ultrasound machine to a GPU-equipped system. A secondary display running parallel to the original ultrasound screen visualized the segmented median nerve and computed the CSA values in real time. RESULTS: margin, confirming a strong alignment with expert clinical assessments. MNSeg-Net demonstrated real-time performance by processing up to 43 frames per second on a single GPU, successfully segmenting the median nerve and computing CSA from ultrasound frames. CONCLUSION: The developed MNSeg-Net-based clinical system represents an important step toward real-time median nerve assessment, enabling a fully automated solution for CTS diagnosis. By combining a lightweight architecture, real-time processing capability, and successful clinical deployment, it represents a substantial advancement in the CTS detection and management.