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Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study

Abstract

Aims 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. Methods 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. Results 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. Conclusion 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.
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Category

Academic article

Language

English

Author(s)

  • Ivar Mjåland Salte
  • Andreas Østvik
  • Sindre Hellum Olaisen
  • Sigve Karlsen
  • Thomas Dahlslett
  • Erik Smistad
  • Torfinn Kirknes Eriksen-Volnes
  • Harald Brunvand
  • Kristina Ingrid Helena Hermann Haugaa
  • Thor Edvardsen
  • Håvard Dalen
  • Lasse Løvstakken
  • Bjørnar Leangen Grenne

Affiliation

  • SINTEF Digital / Health Research
  • Karolinska University Hospital
  • University of Oslo
  • Nord Trondelag Hospital Trust
  • St. Olavs Hospital, Trondheim University Hospital
  • Norwegian University of Science and Technology
  • Sørlandet Hospital Trust
  • Oslo University Hospital

Year

2023

Published in

Journal of the American Society of Echocardiography

ISSN

0894-7317

Volume

36

Issue

7

Page(s)

788 - 799

View this publication at Norwegian Research Information Repository