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Machine learning derived echocardiographic image quality in patients with left ventricular systolic dysfunction: insights on the echo views of greatest image quality

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Abstract

We sought to determine the cardiac ultrasound view of greatest quality using a machine learning (ML) approach on a cohort of transthoracic echocardiograms (TTE) with abnormal left ventricular (LV) systolic function. We utilize an ML model to determine the TTE view of highest quality when scanned by sonographers. A random sample of TTEs with reported LV dysfunction from 09/25/2017-01/15/2019 were downloaded from the regional database. Component video files were analyzed using ML models that jointly classified view and image quality. The model consisted of convolutional layers for extracting spatial features and Long Short-term Memory units to temporally aggregate the frame-wise spatial embeddings. We report the view-specific quality scores for each TTE. Pair-wise comparisons amongst views were performed with Wilcoxon signed-rank test. Of 1,145 TTEs analyzed by the ML model, 74.5% were from males and mean LV ejection fraction was 43.1 ± 9.9%. Maximum quality score was best for the apical 4 chamber (AP4) view (70.6 ± 13.9%, p<0.001 compared to all other views) and worst for the apical 2 chamber (AP2) view (60.4 ± 15.4%, p<0.001 for all views except parasternal short-axis view at mitral/papillary muscle level, PSAX M/PM). In TTEs scanned by professional sonographers, the view with greatest ML-derived quality was the AP4 view.

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Availability of data and material

The data that support the findings of this study are available on request from the corresponding author, CL. The data is available subject to approval by the University of British Columbia Clinical Research Ethics Board.

Abbreviations

AP2:

apical 2 chamber view

AP3:

apical 3 chamber view

AP4:

apical 4 chamber view

CPACS:

Cardiology Picture Archiving and Communications System

LSTM:

long short-term memory model

PLAX:

parasternal long-axis view

PSAX M/PM:

parasternal short-axis view at the level of the mitral valve or papillary muscle

POCUS:

point of care ultrasound

RWMA:

regional wall motion abnormality

TTE:

transthoracic echocardiogram

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Funding

This work was supported by the Vancouver Coastal Health Research Institute and Canadian Institutes of Health Research.

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Authors and Affiliations

  1. Division of Cardiology, University of British Columbia, Vancouver, BC, Canada

    Christina L. Luong, Darwin F. Yeung, Michael Y. C. Tsang, Kenneth Gin, John Jue, Parvathy Nair & Teresa S. M. Tsang

  2. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada

    Delaram Behnami, Nathan Van Woudenberg & Purang Abolmaesumi

  3. University of Adelaide, Australian Institute for Machine Learning, Adelaide, South Australia, Australia

    Zhibin Liao

  4. Arthritis Research Canada, Vancouver, BC, Canada

    Eric C. Sayre

  5. Provincial Health Services Authority, Vancouver, BC, Canada

    Dale Hawley

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  1. Christina L. Luong
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Contributions

C. Luong and D. Behnami contributed equally to this manuscript as co-first authors. T. Tsang and P. Abolmaesumi contributed equally to this manuscript as co-senior authors.

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Correspondence to Christina L. Luong.

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The authors have no relevant financial or non-financial interests to disclose.

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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the University of British Columbia.

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Co-first authors: Christina L. Luong and Delaram Behnami contributed equally to this manuscript.

Co-senior authors: Purang Abolmaesumi and Teresa S. M. Tsang

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Luong, C.L., Behnami, D., Liao, Z. et al. Machine learning derived echocardiographic image quality in patients with left ventricular systolic dysfunction: insights on the echo views of greatest image quality. Int J Cardiovasc Imaging 39, 1313–1321 (2023). https://doi.org/10.1007/s10554-023-02802-4

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