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Automated estimation of echocardiogram image quality in hospitalized patients

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Abstract

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

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The data will not be published publicly but can be made available for facilitation of review.

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Funding

Canadian Institutes of Health Research (CIHR). Natural Sciences and Engineering Research Council of Canada (NSERC).

Author information

Author notes

  1. Christina Luong, Zhibin Liao and Amir Abdi are Joint first authors.

  2. Purang Abolmaesumi and Teresa S. M. Tsang are Joint senior authors.

Authors and Affiliations

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

    Christina Luong, Hany Girgis, Kenneth Gin, John Jue, Darwin Yeung, Michael Yin-Cheung Tsang, Pui Kee Lee, Parvathy Nair & Teresa S. M. Tsang

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

    Zhibin Liao, Amir Abdi, Robert Rohling & Purang Abolmaesumi

  3. Emmes Canada, Burnaby, BC, Canada

    Elena Szefer & Darby Thompson

  4. Diamond Health Care Centre, University of British Columbia, 9th Floor Cardiology, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada

    Teresa S. M. Tsang

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  1. Christina Luong
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Corresponding author

Correspondence to Teresa S. M. Tsang.

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The authors do not have any relevant conflicts of interest to disclose.

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This study was reviewed and approved by the University of British Columbia Clinical Review Ethics Board.

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Luong, C., Liao, Z., Abdi, A. et al. Automated estimation of echocardiogram image quality in hospitalized patients. Int J Cardiovasc Imaging 37, 229–239 (2021). https://doi.org/10.1007/s10554-020-01981-8

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