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

Salte, Ivar Mjåland; Østvik, Andreas; Olaisen, Sindre Hellum; Karlsen, Sigve; Dahlslett, Thomas; Smistad, Erik; Eriksen-Volnes, Torfinn Kirknes; Brunvand, Harald; Haugaa, Kristina H.; Edvardsen, Thor; Dalen, Håvard; Løvstakken, Lasse; Grenne, Bjørnar

doi:10.1016/j.echo.2023.02.017

Cited by 18 publications

(2 citation statements)

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“…Furthermore, it is the first study to show the distribution of view standardization and the potential benefits of real-time guidance to optimize the standardization of apical views. Even though comparative studies are lacking, previous studies from our centre and elsewhere do not show the poorer standardization of recordings at our centre, 5 , 15 , 16 indicating a potential to improve the quality in echocardiographic laboratories across the world.…”

Section: Discussionmentioning

confidence: 59%

Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions

Sabo,

Pasdeloup,

Pettersen

et al. 2023

European Heart Journal - Imaging Methods and Practice

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Aims Impaired standardization of echocardiograms may increase inter-operator variability. This study aimed to determine whether the real-time guidance of experienced sonographers by deep learning (DL) could improve the standardization of apical recordings. Methods and results Patients (n = 88) in sinus rhythm referred for echocardiography were included. All participants underwent three examinations, whereof two were performed by sonographers and the third by cardiologists. In the first study period (Period 1), the sonographers were instructed to provide echocardiograms for the analyses of the left ventricular function. Subsequently, after brief training, the DL guidance was used in Period 2 by the sonographer performing the second examination. View standardization was quantified retrospectively by a human expert as the primary endpoint and the DL algorithm as the secondary endpoint. All recordings were scored in rotation and tilt both separately and combined and were categorized as standardized or non-standardized. Sonographers using DL guidance had more standardized acquisitions for the combination of rotation and tilt than sonographers without guidance in both periods (all P ≤ 0.05) when evaluated by the human expert and DL [except for the apical two-chamber (A2C) view by DL evaluation]. When rotation and tilt were analysed individually, A2C and apical long-axis rotation and A2C tilt were significantly improved, and the others were numerically improved when evaluated by the echocardiography expert. Furthermore, all, except for A2C rotation, were significantly improved when evaluated by DL (P < 0.01). Conclusion Real-time guidance by DL improved the standardization of echocardiographic acquisitions by experienced sonographers. Future studies should evaluate the impact with respect to variability of measurements and when used by less-experienced operators. ClinicalTrials.gov Identifier NCT04580095

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Section: Discussionmentioning

confidence: 59%

Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions

Sabo,

Pasdeloup,

Pettersen

et al. 2023

European Heart Journal - Imaging Methods and Practice

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“…This group has also demonstrated reduced test-retest variability in repeated echocardiograms for GLS analysis by AI as compared to manual measurements. 10 Importantly, these studies did not externally validate their algorithms, which is a critical step before clinical implementation. Our results extend on these previous analyses by including two large external validation cohorts for GLS and demonstrating the feasibility of automated regional strain to identify patients with suspected AMI.…”

Section: Discussionmentioning

confidence: 99%

External validation of a deep learning algorithm for automated echocardiographic strain measurements

Myhre,

Hung,

Frost

et al. 2023

European Heart Journal - Digital Health

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Aims Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging. Methods and results We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson’s correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean ± SD): −18.9 ± 4.5% vs. −18.2 ± 4.4%, respectively, bias 0.68 ± 2.52%, MAD 2.0 ± 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and −15.4 ± 4.1% vs. −15.9 ± 3.6%, respectively, bias −0.65 ± 2.71%, MAD 2.19 ± 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80. Conclusion DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.

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EchoTracker: Advancing Myocardial Point Tracking in Echocardiography

Azad,

Chernyshov,

Nyberg

et al. 2024

Lecture Notes in Computer Science

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

Cited by 18 publications

References 24 publications

Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions

Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions

External validation of a deep learning algorithm for automated echocardiographic strain measurements

EchoTracker: Advancing Myocardial Point Tracking in Echocardiography

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