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Artificial intelligence-based detection of atrial fibrillation from chest radiographs

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

The purpose of this study was to develop an artificial intelligence (AI)–based model to detect features of atrial fibrillation (AF) on chest radiographs.

Methods

This retrospective study included consecutively collected chest radiographs of patients who had echocardiography at our institution from July 2016 to May 2019. Eligible radiographs had been acquired within 30 days of the echocardiography. These radiographs were labeled as AF–positive or AF–negative based on the associated electronic medical records; then, each patient was randomly divided into training, validation, and test datasets in an 8:1:1 ratio. A deep learning–based model to classify radiographs as with or without AF was trained on the training dataset, tuned with the validation dataset, and evaluated with the test dataset.

Results

The training dataset included 11,105 images (5637 patients; 3145 male, mean age ± standard deviation, 68 ± 14 years), the validation dataset included 1388 images (704 patients, 397 male, 67 ± 14 years), and the test dataset included 1375 images (706 patients, 395 male, 68 ± 15 years). Applying the model to the validation and test datasets gave a respective area under the curve of 0.81 (95% confidence interval, 0.78–0.85) and 0.80 (0.76–0.84), sensitivity of 0.76 (0.70–0.81) and 0.70 (0.64–0.76), specificity of 0.75 (0.72–0.77) and 0.74 (0.72–0.77), and accuracy of 0.75 (0.72–0.77) and 0.74 (0.71–0.76).

Conclusion

Our AI can identify AF on chest radiographs, which provides a new way for radiologists to infer AF.

Key Points

• A deep learning–based model was trained to detect atrial fibrillation in chest radiographs, showing that there are indicators of atrial fibrillation visible even on static images.

• The validation and test datasets each gave a solid performance with area under the curve, sensitivity, and specificity of 0.81, 0.76, and 0.75, respectively, for the validation dataset, and 0.80, 0.70, and 0.74, respectively, for the test dataset.

• The saliency maps highlighted anatomical areas consistent with those reported for atrial fibrillation on chest radiographs, such as the atria.

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Data availability

Data generated or analyzed during the study are available from the corresponding author by request.

Abbreviations

AF:

Atrial fibrillation

AI:

Artificial intelligence

AUC:

Area under the curve

LVEF:

Left ventricular ejection fraction

ROC:

Receiver operating characteristic curve

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan

    Toshimasa Matsumoto, Shannon L. Walston, Yasuhito Mitsuyama, Yukio Miki & Daiju Ueda

  2. Department of Cardiovascular Medicine, Graduate School of Medicine, Osaka City University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan

    Shoichi Ehara

  3. Smart Life Science Lab, Center for Health Science Innovation, Osaka City University, 3-1 Ofuka-cho, Kita-ku, Osaka, 530-0011, Japan

    Daiju Ueda

Authors
  1. Toshimasa Matsumoto
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  2. Shoichi Ehara
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  4. Yasuhito Mitsuyama
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  5. Yukio Miki
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  6. Daiju Ueda
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Corresponding author

Correspondence to Daiju Ueda.

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Guarantor

The scientific guarantor of this publication is Daiju Ueda.

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• model development study

• performed at one institution

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Matsumoto, T., Ehara, S., Walston, S.L. et al. Artificial intelligence-based detection of atrial fibrillation from chest radiographs. Eur Radiol 32, 5890–5897 (2022). https://doi.org/10.1007/s00330-022-08752-0

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  • DOI: https://doi.org/10.1007/s00330-022-08752-0

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