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Deep learning to estimate lung disease mortality from chest radiographs

Author

Listed:
  • Jakob Weiss

    (Harvard Medical School, Harvard Institutes of Medicine
    Harvard Medical School, 75 Francis Street and 450 Brookline Avenue
    University Medical Center Freiburg, Faculty of Medicine, University of Freiburg
    Harvard Medical School)

  • Vineet K. Raghu

    (Harvard Medical School, Harvard Institutes of Medicine
    Harvard Medical School)

  • Dennis Bontempi

    (Harvard Medical School, Harvard Institutes of Medicine
    Maastricht University)

  • David C. Christiani

    (Harvard T.H. Chan School of Public Health
    Harvard Medical School)

  • Raymond H. Mak

    (Harvard Medical School, Harvard Institutes of Medicine
    Harvard Medical School, 75 Francis Street and 450 Brookline Avenue)

  • Michael T. Lu

    (Harvard Medical School, Harvard Institutes of Medicine
    Harvard Medical School)

  • Hugo J.W.L. Aerts

    (Harvard Medical School, Harvard Institutes of Medicine
    Harvard Medical School, 75 Francis Street and 450 Brookline Avenue
    Harvard Medical School
    Harvard Medical School)

Abstract

Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limited. Here, we developed a deep learning model, CXR Lung-Risk, to predict the risk of lung disease mortality from a chest x-ray. The model was trained using 147,497 x-ray images of 40,643 individuals and tested in three independent cohorts comprising 15,976 individuals. We found that CXR Lung-Risk showed a graded association with lung disease mortality after adjustment for risk factors, including age, smoking, and radiologic findings (Hazard ratios up to 11.86 [8.64–16.27]; p

Suggested Citation

  • Jakob Weiss & Vineet K. Raghu & Dennis Bontempi & David C. Christiani & Raymond H. Mak & Michael T. Lu & Hugo J.W.L. Aerts, 2023. "Deep learning to estimate lung disease mortality from chest radiographs," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37758-5
    DOI: 10.1038/s41467-023-37758-5
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    References listed on IDEAS

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    1. Roman Zeleznik & Borek Foldyna & Parastou Eslami & Jakob Weiss & Ivanov Alexander & Jana Taron & Chintan Parmar & Raza M. Alvi & Dahlia Banerji & Mio Uno & Yasuka Kikuchi & Julia Karady & Lili Zhang &, 2021. "Deep convolutional neural networks to predict cardiovascular risk from computed tomography," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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