Automated segmentation of the median nerve in patients with carpal tunnel syndrome

• Published in: Scientific reports 2024-07, Vol.14 (1), p.16757-9, Article 16757
• Main Authors: Moser, Florentin, Muller, Sébastien, Lie, Torgrim, Langø, Thomas, Hoff, Mari
• Format: Article
• Language: English
• Subjects: 631/114/1564; 692/4023/1671; Adult; Aged; Algorithms; Automation; Carpal tunnel syndrome; Carpal Tunnel Syndrome - diagnostic imaging; Case-Control Studies; Deep Learning; Female; Forearm; Hospitals; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Machine Learning; Male; Median nerve; Median Nerve - diagnostic imaging; Median Nerve - physiopathology; Medical research; Middle Aged; multidisciplinary; Overuse injuries; Patients; Science; Science (multidisciplinary); Segmentation; Surgery; Ultrasonic imaging; Ultrasonography - methods
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-65840-5

Machine learning and deep learning are novel methods which are revolutionizing medical imaging. In our study we trained an algorithm with a U-Net shaped network to recognize ultrasound images of the median nerve in the complete distal half of the forearm and to measure the cross-sectional area at the inlet of the carpal tunnel. Images of 25 patient hands with carpal tunnel syndrome (CTS) and 26 healthy controls were recorded on a video loop covering 15 cm of the distal forearm and 2355 images were manually segmented. We found an average Dice score of 0.76 between manual and automated segmentation of the median nerve in its complete course, while the measurement of the cross-sectional area at the carpal tunnel inlet resulted in a 10.9% difference between manually and automated measurements. We regard this technology as a suitable device for verifying the diagnosis of CTS.