Automating Angle Measurements on Foot Radiographs in Young Children: Feasibility and Performance of a Convolutional Neural Network Model.
CNN
Clubfoot
Foot
Malalignment
Pediatric
Radiographs
U-NET
Journal
Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
09
01
2023
accepted:
27
03
2023
revised:
24
03
2023
pmc-release:
01
08
2024
medline:
9
8
2023
pubmed:
26
4
2023
entrez:
26
4
2023
Statut:
ppublish
Résumé
Measurement of angles on foot radiographs is an important step in the evaluation of malalignment. The objective is to develop a CNN model to measure angles on radiographs, using radiologists' measurements as the reference standard. This IRB-approved retrospective study included 450 radiographs from 216 patients (< 3 years of age). Angles were automatically measured by means of image segmentation followed by angle calculation, according to Simon's approach for measuring pediatric foot angles. A multiclass U-Net model with a ResNet-34 backbone was used for segmentation. Two pediatric radiologists independently measured anteroposterior and lateral talocalcaneal and talo-1
Identifiants
pubmed: 37099224
doi: 10.1007/s10278-023-00824-x
pii: 10.1007/s10278-023-00824-x
pmc: PMC10406755
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1419-1430Informations de copyright
© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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