Convolutional Neural Networks for Fully Automated Diagnosis of Cardiac Amyloidosis by Cardiac Magnetic Resonance Imaging.
artificial intelligence
cardiac amyloidosis
diagnostic ability
heart failure
Journal
Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269
Informations de publication
Date de publication:
01 Dec 2021
01 Dec 2021
Historique:
received:
28
09
2021
revised:
24
11
2021
accepted:
24
11
2021
entrez:
24
12
2021
pubmed:
25
12
2021
medline:
25
12
2021
Statut:
epublish
Résumé
We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses. Using CMR data from 502 patients (CA: Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines.
Identifiants
pubmed: 34945740
pii: jpm11121268
doi: 10.3390/jpm11121268
pmc: PMC8705947
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Austrian Society of Cardiology
ID : Reducing costs of segmentation labeling in cardiac MRI using explainable AI
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