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A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation

European Radiology volume 32, pages 1558–1569 (2022)Cite this article

Abstract

Objectives

Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning–based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD).

Methods

We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements.

Results

CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 ± 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements.

Conclusions

The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes.

Key Points

• A deep learning–based automatic CB analysis algorithm for diagnosing and quantitatively evaluating VHD using posterior-anterior chest radiographs was developed and validated.

• Our algorithm (CB_auto) yielded comparable reliability to manual CB drawing (CB_hand) in terms of various CB measurement variables, as confirmed by external validation with datasets from three different hospitals and a public dataset.

• All CB parameters were significantly different between VHD and normal control measurements, and echocardiographic measurements were significantly correlated with CB parameters measured from normal control and VHD CXRs.

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Abbreviations

AI:

Artificial intelligence

APE:

Absolute percentage measurement error

CB:

Cardiovascular border

CB_auto:

Automatic CB drawing

CB_hand:

Manual CB drawing

CT ratio:

Cardiothoracic ratio

CXR:

Chest radiograph

ECHO:

Transthoracic echocardiography

ICC:

Intraclass correlation coefficient

VHD:

Valvular heart disease

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Funding

This work was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (HI18C2383 & HI18C0022).

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Author notes

  1. Cherry Kim and Gaeun Lee contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Korea University Ansan Hospital, Ansan, Korea

    Cherry Kim

  2. Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, Korea

    Gaeun Lee, Gyujun Jeong & June-Goo Lee

  3. Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

    Hongmin Oh & Dong Hyun Yang

  4. Department of Cardiology, Korea University Ansan Hospital, Ansan, Korea

    Sun Won Kim

  5. Department of Radiology, Seoul University Bundang Hospital, Seongnam, Korea

    Eun Ju Chun

  6. Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

    Young-Hak Kim

Authors
  1. Cherry Kim
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  2. Gaeun Lee
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  9. Dong Hyun Yang
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Corresponding author

Correspondence to Dong Hyun Yang.

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Guarantor

The scientific guarantor of this publication is Dong Hyun Yang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Cite this article

Kim, C., Lee, G., Oh, H. et al. A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation. Eur Radiol 32, 1558–1569 (2022). https://doi.org/10.1007/s00330-021-08296-9

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