CoChat
Sign Up
Cited by 11Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare – a proof of concept study for the prediction of coronary artery calcification scores

Open access
Wolff, Justus, Matschinske, Julian, Baumgart, Dietrich, Pytlik, Anne, Keck, Andreas, Natarajan, Arunakiry, von Schacky, Claudio E., Pauling, Josch K. +1 more·2022·Journal of Integrative Bioinformatics
DOI 10.1515/jib-2022-0032PMC PMC9800042
Open this paper in CoChat — free accountOpen PDF

Abstract

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed “CACulator” serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.

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 · 10.1515/jib-2022-0032

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?

  • Approaches such as federated machine learning may improve inclusivity while preserving data privacy across institutions.

    “Approaches such as federated machine learning may improve inclusivity while preserving data privacy across institutions.60”

    From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [60]

    The Introduction says federated learning lets institutions train a joint model without sharing raw patient data, and the Conclusion and outlook says it could enable a more comprehensive patient collective through privacy-preserving access across institutions (page numbers unavailable). This supports broader participation in model training, but the paper does not establish improved inclusivity of patient populations or healthcare access. (LLM, conf=0.83)

    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 42 · Cited by 11

Research works printed but not linked: 39

  1. [3]Okrainec K Banerjee DK Eisenberg MJ Coronary artery disease in the developing world Am Heart J 2004 148 7 15 10.1016/j.ahj.2003.11.027 15215786 · DOI
  2. [5]Burger IM Kass NE Sunshine JH Siegelman SS The use of CT for screening: a national survey of radiologists’ activities and attitudes Radiology 2008 248 160 8 10.1148/radiol.2481071369 18483231 PMC3110656 · DOI
  3. [6]Naghavi M Falk E Hecht HS Jamieson MJ Kaul S Berman D From vulnerable plaque to vulnerable patient—Part III: executive summary of the screening for heart attack prevention and education (SHAPE) task force report Am J Cardiol 2006 98 2 15 10.1016/j.amjcard.2006.03.002 16843744 · DOI
  4. [7]Kim KP Einstein AJ Berrington de González A Coronary artery calcification screening: estimated radiation dose and cancer risk Arch Intern Med 2009 169 1188 94 10.1001/archinternmed.2009.162 19597067 PMC2765044 · DOI
  5. [8]Elias-Smale SE Proença RV Koller MT Kavousi M van Rooij FJA Hunink MG Coronary calcium score improves classification of coronary heart disease risk in the elderly: the Rotterdam study J Am Coll Cardiol 2010 56 1407 14 20946998
  6. [11]Kronmal RA McClelland RL Detrano R Shea S Lima JA Cushman M Risk factors for the progression of coronary artery calcification in asymptomatic subjects: results from the Multi-Ethnic Study of Atherosclerosis (MESA) Circulation 2007 115 2722 30 10.1161/circulationaha.106.674143 17502571 · DOI · Paper page
  7. [13]Panicacci S Donati M Profili F Francesconi P Fanucci L Trading-off machine learning algorithms towards data-driven administrative-socio-economic population health management Computers 2020 10 4 10.3390/computers10010004 · DOI
  8. [14]van der Schaar M Alaa AM Floto A Gimson A Scholtes S Wood A How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 Mach Learn 2020 110 1 14 10.1007/s10994-020-05928-x 33318723 PMC7725494 · DOI
  9. [15]Rehak D Dodds P Lannom L A model and infrastructure for federated learning content repositories Interoperability of web-based educational systems workshop 143 2005
  10. [17]Asad M Moustafa A Yu C A critical evaluation of privacy and security threats in federated learning Sensors 2020 20 7182 10.3390/s20247182 33333854 PMC7765278 · DOI
  11. [18]Konečný J Brendan McMahan H Ramage D Richtárik P Federated optimization: distributed machine learning for on-device intelligence 2016 arXiv [cs.LG], vol 02527. Available from: http://arxiv.org/abs/1610.02527. · arXiv
  12. [19]Konečný J Brendan McMahan H Yu FX Richtárik P Suresh AT Bacon D Federated learning: strategies for improving communication efficiency 2016 arXiv [cs.LG], vol 05492. Available from: http://arxiv.org/abs/1610.05492. · arXiv
  13. [21]Grama M Musat M Muñoz-González L Passerat-Palmbach J Rueckert D Alansary A Robust aggregation for adaptive privacy preserving federated learning in healthcare 2020 arXiv [cs.CR], vol 08294. Available from: http://arxiv.org/abs/2009.08294. · arXiv
  14. [25]Matschinske J Späth J Nasirigerdeh R Torkzadehmahani R Hartebrodt A Orbán B The FeatureCloud AI store for federated learning in biomedicine and beyond 2021 arXiv [cs.LG], vol 05734. Available from: http://arxiv.org/abs/2105.05734. PMC10372562 37436815 · arXiv
  15. [26]Chamikara MAP Bertok P Khalil I Liu D Camtepe S Privacy preserving distributed machine learning with federated learning Comput Commun 2021 171 112 25 10.1016/j.comcom.2021.02.014 · DOI
  16. [27]Adnan M Kalra S Cresswell JC Taylor GW Tizhoosh HR Federated learning and differential privacy for medical image analysis Sci Rep 2022 12 1953 10.1038/s41598-022-05539-7 35121774 PMC8816913 · DOI · Paper page
  17. [28]Kirienko M Sollini M Ninatti G Loiacono D Giacomello E Gozzi N Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI Eur J Nucl Med Mol Imag 2021 48 3791 804 10.1007/s00259-021-05339-7 PMC8041944 33847779 · DOI
  18. [29]Dang TK Lan X Weng J Feng M Federated learning for electronic health records ACM Trans Intell Syst Technol 2022 13 1 17 10.1145/3514500 · DOI
  19. [30]Zerka F Barakat S Walsh S Bogowicz M Leijenaar RTH Jochems A Systematic review of privacy-preserving distributed machine learning from federated databases in health care JCO Clin Cancer Inform 2020 4 184 200 10.1200/cci.19.00047 32134684 PMC7113079 · DOI
  20. [31]Rogers MA Aikawa E Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery Nat Rev Cardiol 2019 16 261 74 10.1038/s41569-018-0123-8 30531869 · DOI
  21. [32]Sandstedt M Henriksson L Janzon M Nyberg G Engvall J De Geer J Evaluation of an AI-based, automatic coronary artery calcium scoring software Eur Radiol 2020 30 1671 8 10.1007/s00330-019-06489-x 31728692 PMC7033052 · DOI
  22. [33]Fruchart J-C New risk factors for atherosclerosis and patient risk assessment Circulation 2004 109 III15 10.1161/01.cir.0000131513.33892.5b 15198961 · DOI
  23. [37]Wolff J Baumbach J Pauling J Keck A A systematic review of economic impact studies of artificial intelligence in healthcare J. Med. Internet Res. 2020 22 e16866 32130134 PMC7059082
  24. [38]Lo SK Lu Q Wang C Paik H-Y Zhu L A systematic literature review on federated machine learning: from A software engineering perspective 2020 arXiv [cs.SE], vol 11354. Available from: http://arxiv.org/abs/2007.11354. · arXiv
  25. [39]Raggi P Prognostic implications of absolute and relative calcium scores Herz 2001 26 252 9 10.1007/pl00002028 11479937 · DOI
  26. [40]Williams JK Adams MR Klopfenstein HS Estrogen modulates responses of atherosclerotic coronary arteries Circulation 1990 81 1680 7 10.1161/01.cir.81.5.1680 2331772 · DOI
  27. [41]Manson JE Allison MA Rossouw JE Carr JJ Langer RD Hsia J Estrogen therapy and coronary-artery calcification N Engl J Med 2007 356 2591 602 10.1056/nejmoa071513 17582069 · DOI · Paper page
  28. [42]Coutinho T Goel K Corrêa de Sá D Kragelund C Kanaya AM Zeller M Central obesity and survival in subjects with coronary artery disease: a systematic review of the literature and collaborative analysis with individual subject data J Am Coll Cardiol 2011 57 1877 86 10.1016/j.jacc.2010.11.058 21545944 · DOI
  29. [43]Flint AJ Rexrode KM Hu FB Glynn RJ Caspard H Manson JE Body mass index, waist circumference, and risk of coronary heart disease: a prospective study among men and women Obes Res Clin Pract 2010 4 e171 81 10.1016/j.orcp.2010.01.001 21116472 PMC2992336 · DOI
  30. [44]Li TY Rana JS Manson JE Willett WC Stampfer MJ Colditz GA Obesity as compared with physical activity in predicting risk of coronary heart disease in women Circulation 2006 113 499 506 10.1161/circulationaha.105.574087 16449729 PMC3210835 · DOI
  31. [45]Logue J Murray HM Welsh P Shepherd J Packard C Macfarlane P Obesity is associated with fatal coronary heart disease independently of traditional risk factors and deprivation Heart 2011 97 564 8 10.1136/hrt.2010.211201 21324888 · DOI
  32. [46]Barry Levine T Levine AB Metabolic Syndrome and Cardiovascular Disease Chichester John Wiley & Sons 2012 512
  33. [47]De Schutter A Lavie CJ Milani RV The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease-the obesity paradox Prog Cardiovasc Dis 2014 56 401 8 10.1016/j.pcad.2013.08.003 24438731 · DOI
  34. [48]Lofgren I Herron K Zern T West K Patalay M Shachter NS Waist circumference is a better predictor than body mass index of coronary heart disease risk in overweight premenopausal women J Nutr 2004 134 1071 6 10.1093/jn/134.5.1071 15113947 · DOI
  35. [49]Chen Q Li L Yi J Huang K Shen R Wu R Waist circumference increases risk of coronary heart disease: evidence from a Mendelian randomization study Mol Genet Genomic Med 2020 8 e1186 10.1002/mgg3.1186 32090477 PMC7196469 · DOI
  36. [50]van Setten J Išgum I Pechlivanis S Tragante V de Jong PA Smolonska J Serum lipid levels, body mass index, and their role in coronary artery calcification: a polygenic analysis Circ Cardiovasc Genet 2015 8 327 33 10.1161/circgenetics.114.000496 25577604 · DOI
  37. [51]Alamir MA Goyfman M Chaus A Dabbous F Tamura L Sandfort V The correlation of dyslipidemia with the extent of coronary artery disease in the multiethnic study of atherosclerosis J Lipids 2018 2018 1 9 10.1155/2018/5607349 PMC5892234 29785308 · DOI
  38. [52]Raggi P Shaw LJ Berman DS Callister TQ Prognostic value of coronary artery calcium screening in subjects with and without diabetes J Am Coll Cardiol 2004 43 1663 9 10.1016/j.jacc.2003.09.068 15120828 · DOI · Paper page
  39. [53]Rumberger JA Simons DB Fitzpatrick LA Sheedy PF Schwartz RS Coronary artery calcium area by electron-beam computed tomography and coronary atherosclerotic plaque area. A histopathologic correlative study Circulation 1995 92 2157 62 10.1161/01.cir.92.8.2157 7554196 · DOI

Authors
Wolff, J., Matschinske, J., Baumgart, D., Pytlik, A., Keck, A., Natarajan, A., von Schacky, C. E., Pauling, J. K., & Baumbach, J.
Affiliations
  • Technical University of Munich (DE)
  • Universität Hamburg (DE)
  • Preventicum Essen , Theodor-Althoff-Str. 47 45133 Essen , Germany
  • Syte – Strategy Institute for Digital Health , Hohe Bleichen 8, 20354 Hamburg , Germany
  • Independent Researcher, Digital Health, Informatics and Data Science , Lower Saxony , Germany
  • Department of Diagnostic and Interventional Radiology , Klinikum rechts der Isar, Technical University of Munich , Ismaningerstr. 22, 81675 Munich , Germany
Corresponding author
Wolff, Justus
ORCID
Venue
Journal of Integrative Bioinformatics
Publisher
Walter de Gruyter GmbH
Published
2022-09-05
Volume
19
Issue
4
ISSN
  • 1432-4385 (ISSN-L)
  • 1613-4516
DOI
10.1515/jib-2022-0032
PMC
PMC9800042
OpenAlex
W4294310544
Cited by
11
References
42
Citations by year
2026: 3 · 2025: 3 · 2023: 5
FWCI
0.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
  • University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, cc-by)
Funders
  • VILLUM Young Investigator Grant (13154)
  • European Union’s Horizon 2020 research and innovation programme (826078)
  • Bavarian Research Institute for Digital Transformation
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
federated learning, privacy-preserving data access, cardiovascular risk prediction, proof of concept
MeSH terms
Machine Learning, Proof of Concept Study, Artificial Intelligence, Coronary Vessels, Delivery of Health Care, Humans

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