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

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 th...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-65840-5