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

Autor:	Salte IM; Department of Medicine, Hospital of Southern Norway, Kristiansand, Norway; Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway., Østvik A; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway., Olaisen SH; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway., Karlsen S; Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Medicine, Hospital of Southern Norway, Arendal, Norway., Dahlslett T; Department of Medicine, Hospital of Southern Norway, Arendal, Norway., Smistad E; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Medical Image Analysis, Health Research, SINTEF Digital, Trondheim, Norway., Eriksen-Volnes TK; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway., Brunvand H; Department of Medicine, Hospital of Southern Norway, Arendal, Norway., Haugaa KH; Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway; Faculty of Medicine, Karolinska Institutet and Cardiovascular Division, Karolinska University Hospital, Stockholm, Sweden., Edvardsen T; Faculty of Medicine, University of Oslo, Oslo, Norway; ProCardio Center for Innovation, Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway., Dalen H; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway., Lovstakken L; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway., Grenne B; Centre for Innovative Ultrasound Solutions and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway. Electronic address: bjornar.grenne@ntnu.no.
Jazyk:	angličtina
Zdroj:	Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography [J Am Soc Echocardiogr] 2023 Jul; Vol. 36 (7), pp. 788-799. Date of Electronic Publication: 2023 Mar 16.
DOI:	10.1016/j.echo.2023.02.017
Abstrakt:	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. (Copyright © 2023 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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