Three-Dimensional Measurement of the Uterus on Magnetic Resonance Images: Development and Performance Analysis of an Automated Deep-Learning Tool.
MRI
artificial intelligence
convolutional neural network
deep learning
measurement
uterus
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
12 Aug 2023
12 Aug 2023
Historique:
received:
03
07
2023
revised:
08
08
2023
accepted:
10
08
2023
medline:
26
8
2023
pubmed:
26
8
2023
entrez:
26
8
2023
Statut:
epublish
Résumé
Uterus measurements are useful for assessing both the treatment and follow-ups of gynaecological patients. The aim of our study was to develop a deep learning (DL) tool for fully automated measurement of the three-dimensional size of the uterus on magnetic resonance imaging (MRI). In this single-centre retrospective study, 900 cases were included to train, validate, and test a VGG-16/VGG-11 convolutional neural network (CNN). The ground truth was manual measurement. The performance of the model was evaluated using the objective key point similarity (OKS), the mean difference in millimetres, and coefficient of determination R
Identifiants
pubmed: 37627920
pii: diagnostics13162662
doi: 10.3390/diagnostics13162662
pmc: PMC10453745
pii:
doi:
Types de publication
Journal Article
Langues
eng
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