Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study.
Artificial intelligence
Echocardiography
Left ventricular function
Reproducibility
Strain
Journal
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
ISSN: 1097-6795
Titre abrégé: J Am Soc Echocardiogr
Pays: United States
ID NLM: 8801388
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
15
02
2023
revised:
22
02
2023
accepted:
22
02
2023
medline:
10
7
2023
pubmed:
19
3
2023
entrez:
18
3
2023
Statut:
ppublish
Résumé
Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudinal strain (GLS) and may improve the clinical utility of echocardiography by reducing user-related variability. The aim of this study was to assess within-patient test-retest reproducibility of LV GLS measured by the novel AI method in repeated echocardiograms recorded by different echocardiographers and to compare the results to manual measurements. Two test-retest data sets (n = 40 and n = 32) were obtained at separate centers. Repeated recordings were acquired in immediate succession by 2 different echocardiographers at each center. For each data set, 4 readers measured GLS in both recordings using a semiautomatic method to construct test-retest interreader and intrareader scenarios. Agreement, mean absolute difference, and minimal detectable change (MDC) were compared to analyses by AI. In a subset of 10 patients, beat-to-beat variability in 3 cardiac cycles was assessed by 2 readers and AI. Test-retest variability was lower with AI compared with interreader scenarios (data set I: MDC = 3.7 vs 5.5, mean absolute difference = 1.4 vs 2.1, respectively; data set II: MDC = 3.9 vs 5.2, mean absolute difference = 1.6 vs 1.9, respectively; all P < .05). There was bias in GLS measurements in 13 of 24 test-retest interreader scenarios (largest bias, 3.2 strain units). In contrast, there was no bias in measurements by AI. Beat-to-beat MDCs were 1,5, 2.1, and 2.3 for AI and the 2 readers, respectively. Processing time for analyses of GLS by the AI method was 7.9 ± 2.8 seconds. A fast AI method for automated measurements of LV GLS reduced test-retest variability and removed bias between readers in both test-retest data sets. By improving the precision and reproducibility, AI may increase the clinical utility of echocardiography.
Identifiants
pubmed: 36933849
pii: S0894-7317(23)00139-6
doi: 10.1016/j.echo.2023.02.017
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
788-799Commentaires et corrections
Type : ErratumIn
Informations de copyright
Copyright © 2023 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.