Automated segmentation of the median nerve in patients with carpal tunnel syndrome.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 Jul 2024
20 Jul 2024
Historique:
received:
13
03
2024
accepted:
24
06
2024
medline:
21
7
2024
pubmed:
21
7
2024
entrez:
20
7
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39033223
doi: 10.1038/s41598-024-65840-5
pii: 10.1038/s41598-024-65840-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16757Subventions
Organisme : Joint research Committee
ID : Ref.: 2023/36515
Organisme : The Norwegian Medical Association
ID : Ref.: SAK2022001094
Organisme : Grethe Harbitz Legate
ID : Ref.: P-103661-01
Informations de copyright
© 2024. The Author(s).
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