Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images.
artificial intelligence
coronary artery disease
deep learning
myocardial perfusion imaging
positron emission tomography
Journal
Circulation. Cardiovascular imaging
ISSN: 1942-0080
Titre abrégé: Circ Cardiovasc Imaging
Pays: United States
ID NLM: 101479935
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
entrez:
20
9
2022
pubmed:
21
9
2022
medline:
24
9
2022
Statut:
ppublish
Résumé
We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing. A total of 4735 consecutive patients referred for stress and rest In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77-0.86]) was higher than ischemia (0.60 [95% CI, 0.54-0.66]; The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.
Sections du résumé
BACKGROUND
We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing.
METHODS
A total of 4735 consecutive patients referred for stress and rest
RESULTS
In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77-0.86]) was higher than ischemia (0.60 [95% CI, 0.54-0.66];
CONCLUSIONS
The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.
Identifiants
pubmed: 36126124
doi: 10.1161/CIRCIMAGING.122.014526
pmc: PMC10035936
mid: NIHMS1838072
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e014526Subventions
Organisme : NHLBI NIH HHS
ID : R01 HL089765
Pays : United States
Commentaires et corrections
Type : CommentIn
Type : ErratumIn
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