CoChat
Sign Up
Cited by 44Cardiac Imaging and DiagnosticsArtificial Intelligence in Healthcare and EducationMedical Image Segmentation Techniques

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

Open access
Miller, Robert J. H., Killekar, Aditya, Shanbhag, Aakash, Bednarski, Bryan, Michalowska, Anna M., Ruddy, Terrence D., Einstein, Andrew J., Newby, David E. +9 more·2024·Nature Communications
DOI 10.1038/s41467-024-46977-3PMC PMC10980695
Open this paper in CoChat — free accountOpen PDF

Abstract

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-024-46977-3

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI-CAC could support evolution toward a more informative, physiologically grounded CAC score, preserving clinical familiarity and, combined with radiomics features or additional image-derived markers such as epicardial adipose tissue, cardiac chamber volumetry, as well as complementary biomarkers such as thoracic aortic calcification and body composition markers if these are shown to be predictive

    “AI-CAC could support evolution toward a more informative, physiologically grounded CAC score, preserving clinical familiarity and, combined with radiomics features or additional image-derived markers such as epicardial adipose tissue, cardiac chamber volumetry, as well as complementary biomarkers such as thoracic aortic calcification and body composition markers if these are shown to be predictive”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [56]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 51 · Cited by 44

Research works printed but not linked: 46

  1. [1]Gulati M 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain: a report of the American college of cardiology/american heart association joint committee on clinical practice guidelines Circulation 2021 144 e368 e454 34709879
  2. [2]Knuuti J 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes: the task force for the diagnosis and management of chronic coronary syndromes of the European society of cardiology (ESC) Eur. Heart J. 2020 41 407 477 10.1093/eurheartj/ehz425 31504439 · DOI · Paper page
  3. [3]Agatston AS Quantification of coronary artery calcium using ultrafast computed tomography J. Am. Coll. Cardiol. 1990 15 827 832 10.1016/0735-1097(90)90282-T 2407762 · DOI · Paper page
  4. [4]Budoff MJ Blankstein R Nasir K Blaha MJ Power of zero stronger than “soft” plaque J. Cardiovasc. Comp. Tomogr. 2020 14 279 10.1016/j.jcct.2019.11.005 31848076 · DOI
  5. [5]Margolis JR The diagnostic and prognostic significance of coronary artery calcification. a report of 800 cases Radiology 1980 137 609 616 10.1148/radiology.137.3.7444045 7444045 · DOI
  6. [6]Greenland P Blaha MJ Budoff MJ Erbel R Watson KE Coronary calcium score and cardiovascular risk J. Am. Coll. Cardiol. 2018 72 434 447 10.1016/j.jacc.2018.05.027 30025580 PMC6056023 · DOI
  7. [7]Simons DB Noninvasive definition of anatomic coronary artery disease by ultrafast computed tomographic scanning: a quantitative pathologic comparison study J. Am. Coll. Cardiol. 1992 20 1118 1126 10.1016/0735-1097(92)90367-V 1401612 · DOI
  8. [8]Blaha MJ Blankstein R Nasir K Coronary artery calcium scores of zero and establishing the concept of negative risk factors J. Am. Coll. Cardiol. 2019 74 12 14 10.1016/j.jacc.2019.05.032 31272536 · DOI
  9. [11]Rozanski A Impact of coronary artery calcium scanning on coronary risk factors and downstream testing J. Am. Coll. Cardiol. 2011 57 1622 1632 10.1016/j.jacc.2011.01.019 21439754 PMC3104928 · DOI
  10. [12]Gupta A The identification of calcified coronary plaque is associated with initiation and continuation of pharmacological and lifestyle preventive therapies: a systematic review and meta-analysis JACC Cardiovasc. Imaging. 2017 10 833 842 10.1016/j.jcmg.2017.01.030 28797402 PMC5761651 · DOI · Paper page
  11. [13]Arsanjani R Left ventricular function and volume with coronary CT angiography improves risk stratification and identification of patients at risk for incident mortality: results from 7758 patients in the prospective multinational CONFIRM observational cohort study Radiology 2014 273 70 77 10.1148/radiol.14122816 24991988 · DOI
  12. [14]Kawel-Boehm N Left ventricular mass at MRI and long-term risk of cardiovascular events: the multi-ethnic study of atherosclerosis (MESA) Radiology 2019 293 107 114 10.1148/radiol.2019182871 31453766 PMC6776886 · DOI
  13. [15]Berrington de González A Projected cancer risks from computed tomographic scans performed in the United States in 2007 Arch. Int. Med. 2009 169 2071 2077 10.1001/archinternmed.2009.440 20008689 PMC6276814 · DOI
  14. [16]Hecht HS 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the Society of cardiovascular computed tomography and society of thoracic radiology J. cardiovasc. comp. Tomogr. 2017 11 74 84 10.1016/j.jcct.2016.11.003 27916431 · DOI · Paper page
  15. [17]Budoff MJ Coronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD gene cohort J. Cardiovasc. Comp. Tomogr. 2011 5 113 118 10.1016/j.jcct.2010.11.002 PMC3075464 21167806 · DOI · Paper page
  16. [18]de Vos BD Lessmann N de Jong PA Išgum I Deep learning-quantified calcium scores for automatic cardiovascular mortality prediction at lung screening low-dose CT Radio. Cardiothorac. Imaging 2021 3 e190219 e190219 10.1148/ryct.2021190219 PMC8098090 33969304 · DOI
  17. [21]Wasserthal, J. et al. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. Radiol. Artif. Intell.5, 1–9 (2023). PMC10546353 37795137
  18. [22]Miller, R. J., et al. Deep learning coronary artery calcium scores from SPECT/CT attenuation maps improves prediction of major adverse cardiac events. J. Nucl. Med. 64, 652–658 (2023). PMC10071789 36207138
  19. [23]Pieszko, K., et al. Deep learning of coronary calcium scores from PET/CT attenuation maps accurately predicts adverse cardiovascular events. JACC Cardiovasc. Imaging.16, 675–687 (2023). PMC13016421 36284402
  20. [24]Pieszko K Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans Eur. J. Nucl. Med Mol. Imaging. 2022 49 4122 4132 10.1007/s00259-022-05866-x 35751666 PMC9529893 · DOI
  21. [25]Smith-Bindman R Trends in use of medical Imaging in US health care systems and in Ontario, Canada, 2000-2016 JAMA 2019 322 843 856 10.1001/jama.2019.11456 31479136 PMC6724186 · DOI
  22. [26]Dempster E Cartlidge T Rofe R Neary P Clinical implications of incidental cardiac findings on non-cardiac CT thorax Clin. Radiol. 2020 75 e2 10.1016/j.crad.2020.11.014 · DOI
  23. [27]Sandhu AT Incidental coronary artery calcium: opportunistic screening of previous nongated chest computed tomography scans to improve statin rates (NOTIFY-1 Project) Circulation 2023 147 703 714 10.1161/CIRCULATIONAHA.122.062746 36342823 PMC10108579 · DOI · Paper page
  24. [28]Pieszko, K., et al. Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks. Proc. SPIE Int. Soc. Opt. 12032 10.1117/12.2613147 (2022). PMC9585987 36277935 · DOI
  25. [29]Klein R Ametepe ES Yam Y Dwivedi G Chow BJ Cardiac CT assessment of left ventricular mass in mid-diastasis and its prognostic value Eur. Heart J. Cardiovasc. Imaging 2016 18 95 102 10.1093/ehjci/jev357 26850628 · DOI
  26. [30]Abdi-Ali A LV mass independently predicts mortality and need for future revascularization in patients undergoing diagnostic coronary angiography JACC Cardiovasc. Imaging. 2018 11 423 433 10.1016/j.jcmg.2017.04.012 28734928 · DOI
  27. [31]Chiles C Association of coronary artery calcification and mortality in the national lung screening trial: a comparison of three scoring methods Radiology 2015 276 82 90 10.1148/radiol.15142062 25759972 PMC5137795 · DOI · Paper page
  28. [32]Perneger TV What’s wrong with bonferroni adjustments BMJ 1998 316 1236 1238 10.1136/bmj.316.7139.1236 9553006 PMC1112991 · DOI · Paper page
  29. [33]National Lung Screening Trial Research, T. The national lung screening trial: overview and study design Radiology 2011 258 243 253 10.1148/radiol.10091808 21045183 PMC3009383 · DOI
  30. [34]Rozanski A Impact of coronary artery calcium scanning on coronary risk factors and downstream testing the EISNER (Early Identification of subclinical atherosclerosis by noninvasive imaging research) prospective randomized trial J. Am. Coll. Cardiol. 2011 57 1622 1632 10.1016/j.jacc.2011.01.019 21439754 PMC3104928 · DOI
  31. [35]Hicks KA 2017 cardiovascular and stroke endpoint definitions for clinical trials Circulation 2018 137 961 972 10.1161/CIRCULATIONAHA.117.033502 29483172 · DOI · Paper page
  32. [36]Southern DA An administrative data merging solution for dealing with missing data in a clinical registry: adaptation from ICD-9 to ICD-10 BMC Med. Res. Methodol. 2008 8 1 10.1186/1471-2288-8-1 18215293 PMC2244639 · DOI
  33. [37]Isensee F Jaeger PF Kohl SAA Petersen J Maier-Hein KH nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation Nat. Methods 2021 18 203 211 10.1038/s41592-020-01008-z 33288961 · DOI · Paper page
  34. [38]Gheorghe AG Cardiac left ventricular myocardial tissue density, evaluated by computed tomography and autopsy BMC Med. Imaging. 2019 19 29 10.1186/s12880-019-0326-4 30979363 PMC6461811 · DOI
  35. [39]Fuchs A Normal values of left ventricular mass and cardiac chamber volumes assessed by 320-detector computed tomography angiography in the copenhagen general population study Eur. Heart J. Cardiovasc. Imaging. 2016 17 1009 1017 10.1093/ehjci/jev337 26758412 · DOI
  36. [40]Miller RJH Quantitation of poststress change in ventricular morphology improves risk stratification J. Nucl. Med. 2021 62 1582 1590 10.2967/jnumed.120.260141 33712535 PMC8612345 · DOI
  37. [41]Mikkola TS Gissler M Merikukka M Tuomikoski P Ylikorkala O Sex differences in age-related cardiovascular mortality PloS ONE 2013 8 e63347 10.1371/journal.pone.0063347 23700418 PMC3658978 · DOI
  38. [42]Andre S COPD and cardiovascular disease Pulmonology 2019 25 168 176 10.1016/j.pulmoe.2018.09.006 30527374 · DOI
  39. [43]Tancredi M Excess mortality among persons with type 2 diabetes N. Engl. J. Med. 2015 373 1720 1732 10.1056/NEJMoa1504347 26510021 · DOI
  40. [44]Fuchs FD Whelton PK High blood pressure and cardiovascular disease Hypertension 2020 75 285 292 10.1161/HYPERTENSIONAHA.119.14240 31865786 PMC10243231 · DOI · Paper page
  41. [45]Miller RJH Prognostic significance of previous myocardial infarction and previous revascularization in patients undergoing SPECT MPI Int J. Cardiol. 2020 313 9 15 10.1016/j.ijcard.2020.04.012 32349938 · DOI
  42. [46]Rincon F Stroke location and association with fatal cardiac outcomes: northern manhattan Study (NOMAS) Stroke 2008 39 2425 2431 10.1161/STROKEAHA.107.506055 18635863 PMC4112463 · DOI
  43. [47]Gallucci G Tartarone A Lerose R Lalinga AV Capobianco AM Cardiovascular risk of smoking and benefits of smoking cessation J. Thorac. Dis. 2020 12 3866 3876 10.21037/jtd.2020.02.47 32802468 PMC7399440 · DOI
  44. [48]Lee M Han J Statistical methods and models in the analysis of time to event data Ann. Transl. Med. 2020 8 73 10.21037/atm.2019.12.66 32175366 PMC7049056 · DOI
  45. [49]Pencina MJ D’Agostino RB D’Agostino RB Vasan RS Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond Stat. Med. 2008 27 157 172 10.1002/sim.2929 17569110 · DOI · Paper page
  46. [51]Killekar, A., Shanbhag, A. & Slomka Piotr, J. Predicting mortality from AI-based cardiac volumes, mass, and coronary calcium on chest CT. Zenodo10.3390/diagnostics14020125 (2024). PMC10980695 38553462

Authors
Miller, R. J. H., Killekar, A., Shanbhag, A., Bednarski, B., Michalowska, A. M., Ruddy, T. D., Einstein, A. J., Newby, D. E., Lemley, M., Pieszko, K., Van Kriekinge, S. D., Kavanagh, P. B., Liang, J. X., Huang, C., Dey, D., Berman, D. S., & Slomka, P. J.
Affiliations
  • Cedars-Sinai Medical Center (US)
  • Ottawa Heart Institute (CA)
  • NewYork–Presbyterian Hospital (US)
  • British Heart Foundation Centre for Cardiovascular Science (GB)
Corresponding author
Slomka, Piotr J.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-03-29
Volume
15
Issue
1
Pages
2747-2747
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-46977-3
PMC
PMC10980695
OpenAlex
W4393311713
Cited by
44
References
51
Citations by year
2026: 22 · 2025: 15 · 2024: 7
FWCI
11.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc)
Funders
U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (R01EB034586)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
coronary artery calcium, left ventricular mass, left atrial volume, chest CT, all-cause mortality, cardiovascular mortality, cardiovascular risk stratification, artificial intelligence in radiology
MeSH terms
Artificial Intelligence, Calcium, Cardiac Volume, Heart Ventricles, Humans, Tomography, X-Ray Computed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review