UltraMN: Advancing Real-Time Median Nerve Ultrasound Monitoring With a Multitask Deep Learning Framework

OBJECTIVE: This study aims to develop an advanced deep learning framework to overcome the challenges associated with real-time ultrasound monitoring of the median nerve. METHOD: We propose UltraMN, a novel multitask learning model integrating standard plane classification (UltraCLS) and tissue segmentation (UltraSEG) for comprehensive analysis. The dataset includes 446 cases, with 8 videos collected bilaterally for each case, resulting in a total of 3568 videos and 249,985 images across four standardized imaging planes (4-SIP). The classification accuracy of the median nerve was compared among UltraCLS, MedMamba, and FPT models, while precision, recall, F1 scores, and mean Intersection over Union (mIoU) for 4-SIP segmentation were evaluated. Statistical analyses were conducted using Python 3.12.9. RESULTS: UltraMN significantly outperformed MedMamba and FPT. The UltraCLS module achieved a classification accuracy of 95.6%, with precision, recall, and F1 scores exceeding 95.0% across all standard planes. The UltraSEG module achieved an mIoU of 97.6%, demonstrating superior segmentation performance across all imaging planes. CONCLUSION: UltraMN offers a robust and efficient solution for real-time assessment, achieving high classification accuracy and precise segmentation. As a preliminary feasibility study on healthy subjects, this work is based exclusively on ultrasound data of healthy median nerves-its generalizability to pathological scenarios (e.g., carpal tunnel syndrome) requires further validation. It lays the foundation for enhancing clinical workflows in median nerve disorder management, pending subsequent testing on pathological cases.