Deep learning to estimate lung disease mortality from chest radiographs

Jakob Weiss, Vineet K. Raghu, Dennis Bontempi, David C. Christiani, Raymond H. Mak, Michael T. Lu and Hugo J.W.L. Aerts ()
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Jakob Weiss: Harvard Medical School, Harvard Institutes of Medicine
Vineet K. Raghu: Harvard Medical School, Harvard Institutes of Medicine
Dennis Bontempi: Harvard Medical School, Harvard Institutes of Medicine
David C. Christiani: Harvard T.H. Chan School of Public Health
Raymond H. Mak: Harvard Medical School, Harvard Institutes of Medicine
Michael T. Lu: Harvard Medical School, Harvard Institutes of Medicine
Hugo J.W.L. Aerts: Harvard Medical School, Harvard Institutes of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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

Date: 2023
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DOI: 10.1038/s41467-023-37758-5

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