The role of the deep convolutional neural network as an aid to interpreting brain [
18F-DOPA
Convolutional neural networks
PET/CT
Parkinson’s disease
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Sep 2021
Sep 2021
Historique:
received:
25
07
2020
accepted:
12
02
2021
revised:
12
12
2020
pubmed:
10
3
2021
medline:
25
8
2021
entrez:
9
3
2021
Statut:
ppublish
Résumé
To test the performance of a 3D convolutional neural network (CNN) in analysing brain [ We analyzed patients who had undergone [ Ninety-eight patients were enrolled: 43 presented nigro-striatal degeneration and 55 negative cases used as controls. After training on 69 patients, the diagnostic performance of the 3D CNN was then calculated in 29 patients. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 100%, 89%, 100%, 85% and 93%, respectively. When we compared the 3D CNN results with the SOR analysis, we found that the two patients falsely classified as positive by the 3D CNN procedure showed SOR values ≤ 5 3D CNNs are able to interpret [ • [
Identifiants
pubmed: 33686474
doi: 10.1007/s00330-021-07779-z
pii: 10.1007/s00330-021-07779-z
doi:
Substances chimiques
Dihydroxyphenylalanine
63-84-3
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7003-7011Informations de copyright
© 2021. European Society of Radiology.
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