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Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging

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Abstract

Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective analysis of 100 healthy subjects undergoing cardiac MRI from 2012 to 2018, with 10 men and women in the following age groups: 18–30, 31–40, 41–50, 51–60, and 61–80 years old. Subjects underwent 1.5 T, 2D CINE SSFP MRI. 35 pathologic cases from local clinical exams and the SCMR 2015 consensus contours dataset were also analyzed. A fully convolutional network (FCN) similar to U-Net trained on the U.K. Biobank was used to automatically segment LV endocardial and epicardial contours. FCN and manual segmentation were compared using Dice metrics and measurements of end-diastolic volume (EDV), end-systolic volume (ESV), mass (LVM), and ejection fraction (LVEF). Paired t-tests and linear regressions were used to analyze measurement differences with respect to sex and age. Dice metrics (median ± IQR) for n = 135 cases were 0.94 ± 0.04/0.87 ± 0.10 (ED endocardium/ES endocardium). Measurement biases (mean ± SD) among the healthy cohort were − 0.3 ± 10.1 mL for EDV, − 6.7 ± 9.6 mL for ESV, 4.6 ± 6.4% for LVEF, and − 2.2 ± 11.0 g for LVM; biases were independent of sex and age. Biases among the 35 pathologic cases were 0.1 ± 19 mL for EDV, − 4.8 ± 19 mL for ESV, 2.0 ± 7.6% for LVEF, and 1.0 ± 20 g for LVM. In conclusion, automatic segmentation by the Biobank-trained FCN was independent of age and sex. Improvements in end-systolic basal slice detection are needed to decrease bias and improve precision in ESV and LVEF.

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Acknowledgements

The authors thank Bradley Allen, M.D., for providing a set of manually annotated clinical cases.

Funding

National Heart Lung and Blood Institute (HL117888).

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Northwestern University, Chicago, IL, USA

    Vincent Chen

  2. Department of Radiology, Northwestern University, 737 N. Michigan Avenue, Suite 1600, Chicago, IL, 60611, USA

    Vincent Chen, Michael B. Scott & Michael Markl

  3. Department of Radiology, University of Colorado, Denver, CO, USA

    Alex J. Barker

  4. Circle Cardiovascular Imaging, Inc., Calgary, Canada

    Rotem Golan, Qiao Wei & Alireza Sojoudi

  5. Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, South Korea

    Hyungkyu Huh

Authors
  1. Vincent Chen
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  2. Alex J. Barker
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  4. Michael B. Scott
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  5. Hyungkyu Huh
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  7. Alireza Sojoudi
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  8. Michael Markl
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by VC, RG, MS, and HH. The first draft of the manuscript was written by VC and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Michael Markl.

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Conflict of interest

Three authors on this study work for Circle Cardiovascular Imaging (RG, QW, AS). All other authors have no disclosures to report.

Ethical approval

This study was approved by the local institutional review board, and written informed consent was obtained from all participants.

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Chen, V., Barker, A.J., Golan, R. et al. Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging. Int J Cardiovasc Imaging 37, 3539–3547 (2021). https://doi.org/10.1007/s10554-021-02326-9

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  • DOI: https://doi.org/10.1007/s10554-021-02326-9

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