Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep le...

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Vydáno v:Nature communications Ročník 12; číslo 1; s. 715 - 9
Hlavní autoři: Zeleznik, Roman, Foldyna, Borek, Eslami, Parastou, Weiss, Jakob, Alexander, Ivanov, Taron, Jana, Parmar, Chintan, Alvi, Raza M., Banerji, Dahlia, Uno, Mio, Kikuchi, Yasuka, Karady, Julia, Zhang, Lili, Scholtz, Jan-Erik, Mayrhofer, Thomas, Lyass, Asya, Mahoney, Taylor F., Massaro, Joseph M., Vasan, Ramachandran S., Douglas, Pamela S., Hoffmann, Udo, Lu, Michael T., Aerts, Hugo J. W. L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 29.01.2021
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1305
639/705/117
692/308/409
692/4019/2776
692/499
Aged
Arteriosclerosis
Artificial neural networks
Asymptomatic Diseases
Automation
Biomarkers
Calcification (ectopic)
Calcium
Calcium - analysis
Cardiovascular diseases
Cardiovascular Diseases - complications
Cardiovascular Diseases - diagnosis
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - pathology
Chest
Chest Pain - diagnosis
Chest Pain - etiology
Computed tomography
Coronary artery
Coronary vessels
Coronary Vessels - diagnostic imaging
Coronary Vessels - pathology
Deep Learning
Female
Follow-Up Studies
Health risks
Heart Disease Risk Factors
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted - methods
Male
Middle Aged
multidisciplinary
Neural networks
Reproducibility of Results
Retrospective Studies
Risk analysis
Risk Assessment - methods
Risk factors
Robustness
Science
Science (multidisciplinary)
Tomography
Tomography, X-Ray Computed
ISSN:2041-1723, 2041-1723
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Shrnutí:Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health. Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-20966-2