Pith. sign in

REVIEW 3 major objections 6 minor 204 references

Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

T0 review · 3 major / 6 minor · reviewed 2026-07-10 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Federated deep survival models trained across two unmatched Dutch cohorts beat local-only training on cardiovascular risk without sharing patient records.

desk verdict Real two-cohort federated DeepSurv result with honest modest gains; the Rotterdam improvement is directionally consistent but statistically soft under size-weighted FedAvg and overlapping CIs. read the letter →

arxiv 2607.08595 v1 pith:6RKLIRUL submitted 2026-07-09 cs.LG cs.HC

Hyunho Mo , Djura Smits , Mahlet A. Birhanu , Maarten J.G. Leening , Daniel Bos , Pim van der Harst , Esther E. Bron This is my paper · ORCID
classification cs.LGcs.HC
keywords FederatedlearningCardiovasculardiseaseHealthcareAIDeepsurvivalneuralnetworksLifelinesRotterdamStudySurvFedAvg
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Cardiovascular risk tools are usually built on one site's data or on pooled individual records, both of which run into privacy barriers when institutions want to collaborate. This paper shows that a deep survival network can be trained jointly on two real population cohorts that never exchange raw data: the large Lifelines study with self-reported events and the smaller Rotterdam Study with digitally linked clinical outcomes. After twenty rounds of federated averaging, discrimination rose in both cohorts, with the clearer gain on the Rotterdam side that has complete follow-up. The result matters because it suggests a practical route to larger, more generalizable risk models while leaving patient-level data behind institutional walls.

What carries the argument

Horizontal Federated Averaging (FedAvg) of DeepSurv weights: each site trains the same [12,16,16,1] network for 100 local epochs, then a central aggregator computes a sample-size-weighted average of the weights and redistributes it for the next of 20 rounds; raw records never leave their sites.

What would settle it

Re-run the same 10-split protocol after deliberately dropping or randomly flipping a substantial fraction of Lifelines event labels and check whether the Rotterdam C-statistic still rises above its local baseline of 0.728; if the gain disappears, the claim that the large noisy cohort helps the precise one fails.

Watch

Extended reading notes

Core claim

DeepSurv models trained with Federated Averaging across Lifelines (n=148,230, self-reported outcomes) and the Rotterdam Study (n=10,155, digitally linked outcomes) achieve higher C-statistics than identical models trained only on local data: Rotterdam rises from 0.728 to 0.739 and Lifelines from 0.783 to 0.787, with primary evaluation on Rotterdam because of its complete follow-up.

Load-bearing premise

That Lifelines' noisier, self-reported events with midpoint-interval dates still carry enough signal under sample-size-weighted averaging to improve a model judged on Rotterdam's precise clinical outcomes, despite large gaps in age, event rate, and outcome definition.

Editorial extensions

If this is right

  • Institutions that cannot pool patient records can still improve local CVD risk models by exchanging only model weights under FedAvg.
  • A large cohort with imperfect outcome ascertainment can still raise discrimination on a smaller cohort that has gold-standard follow-up.
  • Convergence must be monitored site-by-site; sample-size-weighted averaging can produce non-monotonic trajectories when cohort sizes differ by an order of magnitude.
  • Existing GDPR-compliant cloud workspaces and joint-controller agreements already support this style of multi-cohort deep survival training.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Adaptive early-stopping or quality-aware reweighting of client updates would likely reduce the late-training dip seen on the smaller, higher-quality node.
  • The same harmonization-plus-FedAvg pipeline should transfer to other time-to-event endpoints (cancer, dementia) that face identical privacy and label-noise tensions.
  • If competing-risk models replace simple censoring of non-CVD death, absolute risk calibration in older Rotterdam participants would be the natural next stress test.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The manuscript evaluates federated training of DeepSurv (FedAvg over 20 rounds on Vantage6) across two heterogeneous Dutch cohorts—Lifelines (N=148,230; self-reported CVD with midpoint event times) and the Rotterdam Study (N=10,155; digitally linked clinical outcomes)—without sharing individual-level data. After FHIR-based harmonization of 12 standard CVD predictors, local vs federated discrimination is compared on held-out stratified splits (10 runs, corrected resampled t-test CIs). The primary claim is that federation raises the C-statistic relative to local training: Rotterdam 0.728 (0.717–0.739) → 0.739 (0.728–0.749); Lifelines 0.783 (0.775–0.791) → 0.787 (0.780–0.792), with Rotterdam as the primary evaluation cohort because of complete follow-up.

Significance. If the performance gain is real and transferable, the work is a useful real-world demonstration that federated deep survival models can improve CVD risk prediction across cohorts that differ in size, age, event rate, and outcome ascertainment, while remaining within existing GDPR/governance constraints. Strengths include a genuine multi-site (not pseudo-split) setup, a documented HL7 FHIR harmonization pipeline, fixed architecture/seed, 10 stratified runs with corrected resampled CIs, primary evaluation on the better-labeled cohort, and an unusually candid discussion of size imbalance and label noise. The contribution is incremental relative to the prior MyDigiTwin infrastructure paper, but the multi-cohort evaluation is the right next step for the field.

major comments (3)
  1. [Table 2 / Results §3] Table 2 and §3: The central claim that federation improves discrimination rests on point-estimate increases whose 95% CIs fully overlap (Rotterdam federated point equals the local upper bound; local point equals the federated lower bound). Only separate CIs for each setting are reported; no paired or corrected-resampled test of the federation-minus-local difference is given. Without a formal test of ΔC (or equivalent), the primary claim remains statistically under-supported and should be either tested or substantially softened in the abstract and conclusions.
  2. [Eq. (4) / Figure 2(A) / Discussion] Eq. (4) and Figure 2(A): FedAvg weights updates by sample size, so Lifelines (~15× larger training set) dominates the aggregate. The Rotterdam trajectory peaks near iteration 12 (~0.740) then declines slightly by iteration 20, which the Discussion attributes to growing Lifelines influence. Reporting only the final (iteration-20) model as the federated result, without a pre-specified stopping rule, quality-weighted aggregation, or an ablation that equalizes or down-weights the noisier node, leaves open whether the reported Rotterdam gain is stable transfer of signal or a transient/size-weighting artifact. At minimum, report best-iteration and last-iteration results with a formal comparison, and discuss (or ablate) alternatives to pure nk/n weighting given the known label-quality asymmetry (Section 2.2.2).
  3. [Section 2.2.2 / Discussion] Section 2.2.2 and Discussion: The weakest modeling assumption is that Lifelines self-reported events with midpoint-interval times remain informative under size-weighted FedAvg for a model evaluated on Rotterdam’s precise clinical outcomes, despite large differences in age, event rate (2.6% vs 13.2%), and outcome definition. The paper acknowledges this but does not quantify sensitivity (e.g., event-time noise simulation on Lifelines, or federation with event-type subsets). A sensitivity analysis or explicit bound on how much label noise can be tolerated would make the transfer claim more credible.
minor comments (6)
  1. [Abstract / Table 2] Abstract and Table 2: State explicitly that CIs overlap and that no formal test of the difference was performed (or add the test), so readers are not left to infer statistical significance from non-overlapping language that is not used but easily misread.
  2. [Section 2.3.1] Section 2.3.1: The architecture [12, 16, 16, 1], dropout 0.20, lr 1e-4, batch 1024, 100 local epochs, and 20 rounds are free hyperparameters with no tuning description. Briefly note whether they were fixed a priori from the prior MyDigiTwin work or selected on validation data.
  3. [Section 2.3.3] Section 2.3.3: Median imputation is done independently per site; a short note on missingness rates by cohort would help assess whether imputation differences could affect federation.
  4. [Discussion] Discussion: Competing risks (non-CVD death treated as censoring) is correctly flagged as a limitation for absolute risk in older Rotterdam participants; consider stating that discrimination (C-statistic) is the sole reported metric and is less directly affected than calibration/absolute risk.
  5. [References] References / Katzman et al.: The DeepSurv citation line appears garbled in the reference list (“Ded2008generalepsurv”); please correct the bibliographic entry.
  6. [Figure 2] Figure 2: Add error bars or shaded bands for the 10-run variability at each iteration so the trajectory can be judged against run-to-run noise.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: federated vs local C-statistic comparison is an external empirical evaluation, not a result forced by definition or self-citation.

full rationale

The paper's central claim is that DeepSurv trained with FedAvg across Lifelines and the Rotterdam Study yields higher held-out C-statistics than the same architecture trained locally (Table 2; Results). The C-statistic is a standard discrimination metric computed on independent local test splits (80/10/10 stratified resampling, 10 runs, corrected resampled t-test CIs); it is not defined by the partial log-likelihood objective (Eq. 3) or by the FedAvg weighted average (Eq. 4). Local baselines use only each node's own data before any aggregation, so the reported gains are not tautological. Self-citations to MyDigiTwin / Cadavid et al. (2025) and the Vantage6 platform describe infrastructure, FHIR harmonization, and a prior pseudo-split feasibility study; they do not supply a uniqueness theorem, ansatz, or fitted parameter that forces the multi-cohort performance numbers. Outcome definitions, predictor sets, and cohort characteristics are stated independently of the claimed improvement. Overlapping CIs and sample-size weighting are statistical/causal concerns, not circular reductions of prediction to input. Score 0 is therefore appropriate.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The claim rests on standard survival and FL machinery plus domain choices about predictors, outcomes, imputation, and aggregation. Free parameters are the usual neural-net and training hyperparameters chosen by the authors; axioms are Cox partial likelihood, FedAvg weighting, and the assumption that self-report labels remain useful under federation. No new physical entities are invented.

free parameters (4)
  • DeepSurv architecture [12, 16, 16, 1]
    Hidden width and depth chosen by authors; not derived from data theory.
  • dropout rate 0.20
    Regularization hyperparameter set by hand.
  • Adam learning rate 1e-4, batch size 1024, 100 local epochs, 20 FedAvg rounds
    Training schedule fixed without reported hyperparameter search; affects final C-statistics.
  • median imputation of missing predictors (per site)
    Simple imputation choice that can shift risk scores.
assumptions (5)
  • domain assumption Cox proportional hazards partial log-likelihood is a valid training objective for DeepSurv on these cohorts
    Section 2.3.1 adopts the standard DeepSurv loss; PH and non-competing-risk assumptions are not validated.
  • domain assumption FedAvg sample-size weighting is an appropriate aggregator under large n and label-quality imbalance
    Equation (4); Discussion notes Lifelines dominates later rounds and Rotterdam performance slightly declines after peak.
  • domain assumption Lifelines self-reported CVD with midpoint event times are sufficiently accurate to transfer useful signal
    Section 2.2.2 outcome definition; primary claim depends on this transfer to Rotterdam.
  • domain assumption Horizontal FL with FHIR-harmonized 12 predictors yields comparable feature spaces across sites
    Sections 2.2.1–2.2.3; residual semantic mismatch would undermine federation.
  • standard math Standard neural-net optimization and C-statistic evaluation machinery
    Adam, ReLU, batch norm, concordance index, corrected resampled t-test.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction." pith.science (2026). https://pith.science/paper/6RKLIRUL

@misc{pith2026260708595,
  author       = {Pith},
  title        = {Pith review of: Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6RKLIRUL}},
  note         = {Machine review of arXiv:2607.08595}
}
read the original abstract

Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing patient-level data. Federated learning enables collaborative model development without transferring sensitive patient data, but its application in healthcare remains challenging because datasets often differ in size, population characteristics, and outcome definitions. In this study, we present a federated deep learning approach for privacy-preserving cardiovascular disease risk prediction that integrates two population-based cohorts with different characteristics: Lifelines, including 148,230 participants meeting the study inclusion criteria with self-reported outcomes, and the Rotterdam Study, including a smaller cohort of 10,155 participants with digitally linked clinical outcomes. Model performance was primarily evaluated on the Rotterdam Study because of its complete follow-up. Deep survival models trained using federated learning achieved higher predictive performance than models trained locally without federation. For the Rotterdam Study, the C-statistic increased from 0.728 (95% CI: 0.717-0.739) to 0.739 (95% CI: 0.728-0.749). For Lifelines, the C-statistic increased from 0.783 (95% CI: 0.775-0.791) to 0.787 (95% CI: 0.780-0.792). These findings suggest that federated deep learning across heterogeneous cohorts can improve cardiovascular disease risk prediction while preserving the privacy of individual-level patient data.

Figures

Figures reproduced from arXiv: 2607.08595 by the authors.

Figure 1
Figure 1. Overview of the federated learning setup using the Vantage6 infrastructure. A dedicated [PITH_FULL_IMAGE:figures/full_fig_p015_1.png] view at source ↗
Figure 2
Figure 2. Mean C-statistic across 10 independent runs at each FedAvg update iteration for the [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

204 extracted references · 204 canonical work pages

  1. [1]

    Computers in biology and medicine , volume=

    MyDigiTwin: A privacy-preserving framework for personalized cardiovascular risk prediction and scenario exploration , author=. Computers in biology and medicine , volume=. 2025 , publisher=

    work page 2025

  2. [2]

    2022 , month = sep, langid =

    work page 2022

  3. [3]

    Government of the Netherlands , year =

    What Is a Personal Healthcare Environment? , author =. Government of the Netherlands , year =

    work page

  4. [4]

    Cardiovascular disease risk communication and prevention: a meta-analysis , volume =

    Bakhit, Mina and Fien, Samantha and Abukmail, Eman and Jones, Mark and Clark, Justin and Scott, Anna Mae and Glasziou, Paul and Cardona, Magnolia , urldate =. Cardiovascular disease risk communication and prevention: a meta-analysis , volume =. doi:10.1093/eurheartj/ehae002 , shorttitle =

    work page doi:10.1093/eurheartj/ehae002

  5. [5]

    Hart- en vaatziekten , url =

    work page

  6. [6]

    Studies in Health Technology and Informatics , volume=

    Leveraging FHIR in Federated Learning Environments: A Data Harmonization Framework for Cohort Studies , author=. Studies in Health Technology and Informatics , volume=. 2024 , publisher=

    work page 2024

  7. [7]

    BMC medical research methodology , volume=

    A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures , author=. BMC medical research methodology , volume=. 2012 , publisher=

    work page 2012

  8. [8]

    Zanen, Wouter , urldate =

    work page

Show all 204 references
  1. [9]

    European Health Data Space , url =

    European Commission , urldate =. European Health Data Space , url =

  2. [10]

    Logical observation identifier names and codes (

    Forrey,. Logical observation identifier names and codes (. doi:10.1093/clinchem/42.1.81 , pages =

    doi

  3. [11]

    5-step briefing , url =

  4. [12]

    Communication from the Commission to the European Parliament and the Council: A European Health Data Space: harnessing the power of health data for people, patients and innovation , author =

  5. [13]

    NPJ digital medicine , volume=

    The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field , author=. NPJ digital medicine , volume=. 2022 , publisher=

    2022

  6. [14]

    Annual Review of Pharmacology and Toxicology , volume=

    Health digital twins in life science and health care innovation , author=. Annual Review of Pharmacology and Toxicology , volume=. 2024 , publisher=

    2024

  7. [15]

    Chinese Medical Journal , volume=

    Opportunities and challenges of digital twin technology in healthcare , author=. Chinese Medical Journal , volume=. 2023 , publisher=

    2023

  8. [16]

    arXiv preprint arXiv:2405.09649 , year=

    Challenges and opportunities for digital twins in precision medicine: a complex systems perspective , author=. arXiv preprint arXiv:2405.09649 , year=

    arXiv

  9. [17]

    Journal of the American Heart Association , volume=

    Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact , author=. Journal of the American Heart Association , volume=

  10. [18]

    Transdisciplinary perspectives on complex systems: New findings and approaches , pages=

    Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems , author=. Transdisciplinary perspectives on complex systems: New findings and approaches , pages=. 2017 , publisher=

    2017

  11. [19]

    Data Intelligence , volume=

    Distributed analytics on sensitive medical data: the personal health train , author=. Data Intelligence , volume=. 2020 , publisher=

    2020

  12. [20]

    AMIA annual symposium proceedings , volume=

    VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange , author=. AMIA annual symposium proceedings , volume=. 2020 , organization=

    2020

  13. [21]

    Federated Learning , pages=

    Horizontal federated learning , author=. Federated Learning , pages=. 2020 , publisher=

    2020

  14. [22]

    European journal of public health , volume=

    Benefits and challenges of Big Data in healthcare: an overview of the European initiatives , author=. European journal of public health , volume=. 2019 , publisher=

    2019

  15. [23]

    BMC medical informatics and decision making , volume=

    Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability , author=. BMC medical informatics and decision making , volume=. 2022 , publisher=

    2022

  16. [25]

    Circulation , author =

    Heart. Circulation , author =. 2019 , note =. doi:10.1161/CIR.0000000000000659 , number =

    2019 doi

  17. [26]

    professional.heart.org , month = sep, year =

  18. [27]

    Diabetologia , author =

    Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings , volume =. Diabetologia , author =. 2022 , pmid =. doi:10.1007/s00125-021-05640-y , abstract =

    2022 doi

  19. [28]

    New England Journal of Medicine , volume=

    Estimating glomerular filtration rate from serum creatinine and cystatin C , author=. New England Journal of Medicine , volume=. 2012 , publisher=

    2012

  20. [29]

    2022 , publisher=

    Test driven development: By example , author=. 2022 , publisher=

    2022

  21. [30]

    Foundations and trends

    Advances and open problems in federated learning , author=. Foundations and trends. 2021 , publisher=

    2021

  22. [31]

    ACM Transactions on Intelligent Systems and Technology (TIST) , volume=

    Federated machine learning: Concept and applications , author=. ACM Transactions on Intelligent Systems and Technology (TIST) , volume=. 2019 , publisher=

    2019

  23. [32]

    Artificial intelligence and statistics , pages=

    Communication-efficient learning of deep networks from decentralized data , author=. Artificial intelligence and statistics , pages=. 2017 , organization=

    2017

  24. [33]

    2008 , publisher=

    Applied survival analysis: regression modeling of time-to-event data , author=. 2008 , publisher=

    2008

  25. [34]

    Journal of the Royal Statistical Society: Series B (Methodological) , volume=

    Regression models and life-tables , author=. Journal of the Royal Statistical Society: Series B (Methodological) , volume=. 1972 , publisher=

    1972

  26. [35]

    Jama , volume=

    Evaluating the yield of medical tests , author=. Jama , volume=. 1982 , publisher=

    1982

  27. [36]

    Archives of internal medicine , volume=

    Decade-long trends and factors associated with time to hospital presentation in patients with acute myocardial infarction: the Worcester Heart Attack study , author=. Archives of internal medicine , volume=. 2000 , publisher=

    2000

  28. [37]

    ACM Computing Surveys , volume=

    Heterogeneous federated learning: State-of-the-art and research challenges , author=. ACM Computing Surveys , volume=. 2023 , publisher=

    2023

  29. [38]

    Proceedings of the 3rd International Workshop on Distributed Machine Learning , pages=

    Rethinking normalization methods in federated learning , author=. Proceedings of the 3rd International Workshop on Distributed Machine Learning , pages=

  30. [39]

    2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) , pages=

    Federated Learning Algorithm Handling Missing Attributes , author=. 2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) , pages=. 2023 , organization=

    2023

  31. [40]

    Advances in neural information processing systems , volume=

    Inference for the generalization error , author=. Advances in neural information processing systems , volume=

  32. [41]

    International journal of cardiology , volume=

    The LifeLines Cohort Study: prevalence and treatment of cardiovascular disease and risk factors , author=. International journal of cardiology , volume=. 2017 , publisher=

    2017

  33. [42]

    International journal of epidemiology , volume=

    Cohort Profile: LifeLines, a three-generation cohort study and biobank , author=. International journal of epidemiology , volume=. 2015 , publisher=

    2015

  34. [43]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Local learning matters: Rethinking data heterogeneity in federated learning , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  35. [44]

    Fast Healthcare Interoperability Resources (FHIR) Specification , year =

  36. [45]

    Nature Medicine , volume=

    Federated learning in healthcare: facilitating multi-institutional collaborations without sharing patient data , author=. Nature Medicine , volume=. 2021 , doi=

    2021

  37. [46]

    Mandemakers and Harold Snieder and Ronald P

    Bart Klijs and Salome Scholtens and Jornt J. Mandemakers and Harold Snieder and Ronald P. Stolk and Nynke Smidt , title =. PLOS ONE , volume =. 2015 , doi =

    2015

  38. [47]

    Procedia Computer Science , volume=

    Homomorphic encryption , author=. Procedia Computer Science , volume=. 2013 , publisher=

    2013

  39. [48]

    Electronics , volume=

    Reviewing federated learning aggregation algorithms; strategies, contributions, limitations and future perspectives , author=. Electronics , volume=. 2023 , publisher=

    2023

  40. [49]

    International colloquium on automata, languages, and programming , pages=

    Differential privacy , author=. International colloquium on automata, languages, and programming , pages=. 2006 , organization=

    2006

  41. [50]

    Biometrics , volume=

    Time-dependent ROC curves for censored survival data and a diagnostic marker , author=. Biometrics , volume=. 2000 , publisher=

    2000

  42. [51]

    Proceedings of Machine learning and systems , volume=

    Federated optimization in heterogeneous networks , author=. Proceedings of Machine learning and systems , volume=

  43. [52]

    arXiv preprint arXiv:2102.07623 , year=

    Fedbn: Federated learning on non-iid features via local batch normalization , author=. arXiv preprint arXiv:2102.07623 , year=

    arXiv

  44. [53]

    Advances in neural information processing systems , volume=

    An efficient framework for clustered federated learning , author=. Advances in neural information processing systems , volume=

  45. [54]

    IEEE transactions on neural networks and learning systems , volume=

    Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints , author=. IEEE transactions on neural networks and learning systems , volume=. 2020 , publisher=

    2020

  46. [55]

    Statistics in medicine , volume=

    A neural network model for survival data , author=. Statistics in medicine , volume=. 1995 , publisher=

    1995

  47. [56]

    arXiv preprint arXiv:1412.6980 , year=

    Adam: A method for stochastic optimization , author=. arXiv preprint arXiv:1412.6980 , year=

    arXiv

  48. [57]

    1997 , publisher=

    Analysis of incomplete multivariate data , author=. 1997 , publisher=

    1997

  49. [58]

    Article Title

    Author1 LastName1 and Author2 LastName2 and Author3 LastName3. Article Title. Frontiers in Neuroscience. 2013. doi:10.3389/fnins.2013.12345

    2013 doi

  50. [59]

    Author Name , title =

  51. [60]

    The title of the book , publisher =

    Bauthor Surname , title =. The title of the book , publisher =

  52. [61]

    The title of the conference proceedings , year = 1996, publisher =

    Cauthor Name and Dauthor Surname and Fauthor LastName , title =. The title of the conference proceedings , year = 1996, publisher =

    1996

  53. [62]

    Gauthor Name1 , title =

  54. [63]

    Hauthor Surname1 , title =

  55. [64]

    Data Title

    Author1 LastName1 and Author2 LastName2 and Author3 LastName3. Data Title. 2011. doi:10.000/55555

    2011

  56. [65]

    The Lancet , volume =

    Kathy Leung and Joseph T Wu and Di Liu and Gabriel M Leung , title =. The Lancet , volume =

  57. [66]

    The Lancet Public Health , volume =

    Benjamin J Cowling and Sheikh Taslim Ali and Tiffany W Y Ng and Tim K Tsang and Julian C M Li and Min Whui Fong and Qiuyan Liao and Mike YW Kwan and So Lun Lee and Susan S Chiu and Joseph T Wu and Peng Wu and Gabriel M Leung , title =. The Lancet Public Health , volume =. doi:...

    doi

  58. [67]

    Cardiovascular Imaging , volume=

    When does a calcium score equate to secondary prevention? Insights from the multinational CONFIRM registry , author=. Cardiovascular Imaging , volume=. 2023 , publisher=

    2023

  59. [68]

    Heart, Lung and Circulation , volume=

    Cardiac Society of Australia and New Zealand position statement: coronary artery calcium scoring , author=. Heart, Lung and Circulation , volume=. 2017 , publisher=

    2017

  60. [69]

    Canadian journal of cardiology , volume=

    2021 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention of cardiovascular disease in adults , author=. Canadian journal of cardiology , volume=. 2021 , publisher=

    2021

  61. [70]

    Greenwade

    George D. Greenwade. The C omprehensive T ex A rchive N etwork ( CTAN ). TUGBoat. 1993

    1993

  62. [71]

    Circulation , volume=

    2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines , author=. Circulation , volume=. 2014 , publisher=

    2013

  63. [72]

    PloS one , volume=

    Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants , author=. PloS one , volume=. 2019 , publisher=

    2019

  64. [73]

    International Journal of Epidemiology , volume=

    Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach , author=. International Journal of Epidemiology , volume=. 2022 , publisher=

    2022

  65. [74]

    Circulation , volume=

    General cardiovascular risk profile for use in primary care: the Framingham Heart Study , author=. Circulation , volume=. 2008 , publisher=

    2008

  66. [75]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  67. [76]

    arXiv preprint arXiv:1704.04861 , year=

    Mobilenets: Efficient convolutional neural networks for mobile vision applications , author=. arXiv preprint arXiv:1704.04861 , year=

    arXiv

  68. [77]

    European heart journal , volume=

    Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project , author=. European heart journal , volume=. 2003 , publisher=

    2003

  69. [78]

    European journal of preventive cardiology , volume=

    2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contributi...

    2021

  70. [79]

    Journal of the American College of Cardiology , volume=

    2013 ACC/AHA guideline on the assessment of cardiovascular risk , author=. Journal of the American College of Cardiology , volume=. 2014 , publisher=

    2013

  71. [80]

    bmj , volume=

    Prediction models for cardiovascular disease risk in the general population: systematic review , author=. bmj , volume=. 2016 , publisher=

    2016

  72. [81]

    Journal of Healthcare Informatics Research , volume=

    Multi-cohort federated learning shows synergy in mortality prediction for MRI-based and metabolomics-based age scores , author=. Journal of Healthcare Informatics Research , volume=. 2025 , publisher=

    2025

  73. [82]

    Medical image analysis , volume=

    From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare , author=. Medical image analysis , volume=. 2025 , publisher=

    2025

  74. [83]

    Medical Image Analysis , volume=

    Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge , author=. Medical Image Analysis , volume=. 2024 , publisher=

    2022

  75. [84]

    Knowledge-Based Systems , pages=

    FedERFT: Improving Federated Learning through Feature-Enriched Regularization and Post-Aggregation Fine-Tuning , author=. Knowledge-Based Systems , pages=. 2026 , publisher=

    2026

  76. [85]

    Neurocomputing , pages=

    FedACA: Adaptive classifier aggregation and clustering for personalized heterogeneous federated learning , author=. Neurocomputing , pages=. 2026 , publisher=

    2026

  77. [86]

    Swarm and Evolutionary Computation , volume=

    A multi-objective hierarchical aggregation optimization algorithm for dynamic network structures in federated learning , author=. Swarm and Evolutionary Computation , volume=. 2026 , publisher=

    2026

  78. [87]

    European journal of preventive cardiology , volume=

    Validation and comparison of 28 risk prediction models for coronary artery disease , author=. European journal of preventive cardiology , volume=. 2022 , publisher=

    2022

  79. [88]

    Proceedings of the IEEE , volume=

    Gradient-based learning applied to document recognition , author=. Proceedings of the IEEE , volume=. 1998 , publisher=

    1998

  80. [89]

    IEEE transactions on neural networks , volume=

    On the use of artificial neural networks for the analysis of survival data , author=. IEEE transactions on neural networks , volume=. 1997 , publisher=

    1997

  81. [90]

    2018 , publisher=

    Analysis of survival data , author=. 2018 , publisher=

    2018

  82. [91]

    BMC medical education , volume=

    Revolutionizing healthcare: the role of artificial intelligence in clinical practice , author=. BMC medical education , volume=. 2023 , publisher=

    2023

  83. [92]

    arXiv preprint arXiv:2305.14961 , year=

    Deep Learning for Survival Analysis: A Review , author=. arXiv preprint arXiv:2305.14961 , year=

    arXiv

  84. [93]

    BMC medical research methodology , volume=

    Ded2008generalepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network , author=. BMC medical research methodology , volume=. 2018 , publisher=

    2018

  85. [94]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Deephit: A deep learning approach to survival analysis with competing risks , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  86. [95]

    Computers in Biology and Medicine , volume=

    Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA) , author=. Computers in Biology and Medicine , volume=. 2021 , publisher=

    2021

  87. [96]

    Journal of Hypertension , volume=

    Application of deep neural survival networks to the development of risk prediction models for diabetes mellitus, hypertension, and dyslipidemia , author=. Journal of Hypertension , volume=. 2024 , publisher=

    2024

  88. [97]

    American journal of epidemiology , volume=

    Multi-ethnic study of atherosclerosis: objectives and design , author=. American journal of epidemiology , volume=. 2002 , publisher=

    2002

  89. [98]

    PeerJ , volume=

    A scalable discrete-time survival model for neural networks , author=. PeerJ , volume=. 2019 , publisher=

    2019

  90. [99]

    Jama , volume=

    Recent changes in attack and survival rates of acute myocardial infarction (1975 through 1981): the Worcester Heart Attack Study , author=. Jama , volume=. 1986 , publisher=

    1975

  91. [100]

    Annals of internal medicine , volume=

    The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults , author=. Annals of internal medicine , volume=. 1995 , publisher=

    1995

  92. [101]

    European journal of epidemiology , volume=

    The Rotterdam Study: objectives and design update , author=. European journal of epidemiology , volume=. 2007 , publisher=

    2007

  93. [102]

    European journal of epidemiology , volume=

    The Rotterdam Study: 2018 update on objectives, design and main results , author=. European journal of epidemiology , volume=. 2017 , publisher=

    2018

  94. [103]

    Medical image analysis , volume=

    Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms , author=. Medical image analysis , volume=. 2009 , publisher=

    2009

  95. [104]

    Canadian Journal of Cardiology , volume=

    2016 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention of cardiovascular disease in the adult , author=. Canadian Journal of Cardiology , volume=. 2016 , publisher=

    2016

  96. [105]

    Atherosclerosis , volume=

    Arterial calcification at different sites and prediction of atherosclerotic cardiovascular disease among women and men , author=. Atherosclerosis , volume=. 2021 , publisher=

    2021

  97. [106]

    European Journal of Preventive Cardiology , volume=

    Sex-specific added value of cardiac biomarkers for 10-year cardiovascular risk prediction , author=. European Journal of Preventive Cardiology , volume=. 2022 , publisher=

    2022

  98. [107]

    Heart , volume=

    Improvements in risk stratification for the occurrence of cardiovascular disease by imaging subclinical atherosclerosis: a systematic review , author=. Heart , volume=. 2012 , publisher=

    2012

  99. [108]

    Circulation: Cardiovascular Imaging , volume=

    Carotid plaque composition and prediction of incident atherosclerotic cardiovascular disease , author=. Circulation: Cardiovascular Imaging , volume=. 2022 , publisher=

    2022

  100. [109]

    Annals of internal medicine , volume=

    Evaluation of newer risk markers for coronary heart disease risk classification: a cohort study , author=. Annals of internal medicine , volume=. 2012 , publisher=

    2012

  101. [110]

    Hypertension , volume=

    Amino-terminal pro--B-type natriuretic peptide improves cardiovascular and cerebrovascular risk prediction in the population: the Rotterdam study , author=. Hypertension , volume=. 2010 , publisher=

    2010

  102. [111]

    European heart journal , volume=

    C-reactive protein gene haplotypes and risk of coronary heart disease: the Rotterdam Study , author=. European heart journal , volume=. 2006 , publisher=

    2006

  103. [112]

    ACM Computing Surveys (CSUR) , volume=

    Machine learning for survival analysis: A survey , author=. ACM Computing Surveys (CSUR) , volume=. 2019 , publisher=

    2019

  104. [113]

    Journal of the American statistical association , volume=

    Nonparametric estimation from incomplete observations , author=. Journal of the American statistical association , volume=. 1958 , publisher=

    1958

  105. [114]

    1999 , publisher=

    Iterative methods for optimization , author=. 1999 , publisher=

    1999

  106. [115]

    arXiv preprint arXiv:1711.05101 , year=

    Decoupled weight decay regularization , author=. arXiv preprint arXiv:1711.05101 , year=

    arXiv

  107. [116]

    Circulation , volume=

    Biomarkers of cardiovascular disease: molecular basis and practical considerations , author=. Circulation , volume=. 2006 , publisher=

    2006

  108. [117]

    European journal of epidemiology , volume=

    Methods of data collection and definitions of cardiac outcomes in the Rotterdam Study , author=. European journal of epidemiology , volume=. 2012 , publisher=

    2012

  109. [118]

    Circulation , volume=

    Comparison of conventional lipoprotein tests and apolipoproteins in the prediction of cardiovascular disease: data from UK Biobank , author=. Circulation , volume=. 2019 , publisher=

    2019

  110. [119]

    Atherosclerosis , volume=

    Vertebrobasilar artery calcification: prevalence and risk factors in the general population , author=. Atherosclerosis , volume=. 2019 , publisher=

    2019

  111. [120]

    2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , pages=

    Deep convolutional neural network for survival analysis with pathological images , author=. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , pages=. 2016 , organization=

    2016

  112. [121]

    Projection-wise disentangling for fair and interpretable representation learning: Application to 3d facial shape analysis , author=. Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--Octobe...

    2021

  113. [122]

    Circulation: Cardiovascular Quality and Outcomes , pages=

    Association Between Electrocardiographic Age and Cardiovascular Events in Community Settings: The Framingham Heart Study , author=. Circulation: Cardiovascular Quality and Outcomes , pages=. 2023 , publisher=

    2023

  114. [123]

    Frontiers in Psychiatry , volume=

    Optimising a simple fully convolutional network for accurate brain age prediction in the pac 2019 challenge , author=. Frontiers in Psychiatry , volume=. 2021 , publisher=

    2019

  115. [124]

    Medical image analysis , volume=

    Accurate brain age prediction with lightweight deep neural networks , author=. Medical image analysis , volume=. 2021 , publisher=

    2021

  116. [125]

    Molecular psychiatry , volume=

    Brain age predicts mortality , author=. Molecular psychiatry , volume=. 2018 , publisher=

    2018

  117. [126]

    JACC: Cardiovascular imaging , volume=

    State-of-the-art deep learning in cardiovascular image analysis , author=. JACC: Cardiovascular imaging , volume=. 2019 , publisher=

    2019

  118. [127]

    Journal of the American College of Cardiology , volume=

    Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review , author=. Journal of the American College of Cardiology , volume=. 2019 , publisher=

    2019

  119. [128]

    Frontiers in Cardiovascular Medicine , volume=

    Use of multi-modal data and machine learning to improve cardiovascular disease care , author=. Frontiers in Cardiovascular Medicine , volume=. 2022 , publisher=

    2022

  120. [129]

    Proceedings of the National Academy of Sciences , volume=

    Gray matter age prediction as a biomarker for risk of dementia , author=. Proceedings of the National Academy of Sciences , volume=. 2019 , publisher=

    2019

  121. [130]

    European Heart Journal , volume=

    Cardiovascular disease risk communication and prevention: a meta-analysis , author=. European Heart Journal , volume=. 2024 , publisher=

    2024

  122. [131]

    Diabetologia , volume=

    Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings , author=. Diabetologia , volume=. 2022 , publisher=

    2022

  123. [132]

    bmj , volume=

    Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study , author=. bmj , volume=. 2017 , publisher=

    2017

  124. [133]

    European Heart Journal , volume=

    SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe , author=. European Heart Journal , volume=. 2021 , publisher=

    2021

  125. [134]

    Nature methods , volume=

    nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , author=. Nature methods , volume=. 2021 , publisher=

    2021

  126. [135]

    European Journal of Preventive Cardiology , volume=

    Epicardial fat volume and the risk of cardiometabolic diseases among women and men from the general population , author=. European Journal of Preventive Cardiology , volume=. 2021 , publisher=

    2021

  127. [136]

    Epidemiology , volume=

    Prognostic models with competing risks: methods and application to coronary risk prediction , author=. Epidemiology , volume=. 2009 , publisher=

    2009

  128. [137]

    The American journal of cardiology , volume=

    Electrocardiographic estimate of left ventricular mass versus radiographic cardiac size and the risk of cardiovascular disease mortality in the epidemiologic follow-up study of the First National Health and Nutrition Examination Survey , author=. The American journal of cardio...

    1988

  129. [138]

    Journal of the American Heart Association , volume=

    Epicardial fat volume, cardiac function, and incident heart failure: the Rotterdam study , author=. Journal of the American Heart Association , volume=

  130. [139]

    Proceedings of the IEEE international conference on computer vision , pages=

    Grad-cam: Visual explanations from deep networks via gradient-based localization , author=. Proceedings of the IEEE international conference on computer vision , pages=

  131. [140]

    BMC medicine , volume=

    Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis , author=. BMC medicine , volume=. 2019 , publisher=

    2019

  132. [141]

    EClinicalMedicine , volume=

    SCORE2 cardiovascular risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands: an external validation study , author=. EClinicalMedicine , volume=. 2023 , publisher=

    2023

  133. [142]

    International journal of cardiology , volume=

    Performance of Framingham cardiovascular disease (CVD) predictions in the Rotterdam Study taking into account competing risks and disentangling CVD into coronary heart disease (CHD) and stroke , author=. International journal of cardiology , volume=. 2014 , publisher=

    2014

  134. [143]

    Journal of the American College of Cardiology , volume=

    2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines , author=. Journal of the American College of Cardiology , volume=. 2014 , publisher=

    2013

  135. [144]

    Journal of the American College of Cardiology , volume=

    10-year coronary heart disease risk prediction using coronary artery calcium and traditional risk factors: derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) with validation in the HNR (Heinz Nixdorf Recall) study and the DHS (Dallas Heart Study) , author=. Journal...

    2015

  136. [145]

    Circulation: Cardiovascular Imaging , volume=

    Is there a role for coronary artery calcium scoring for management of asymptomatic patients at risk for coronary artery disease? Clinical risk scores are not sufficient to define primary prevention treatment strategies among asymptomatic patients , author=. Circulation: Cardio...

    2014

  137. [146]

    Journal of the American College of Cardiology , volume=

    Coronary calcium score and cardiovascular risk , author=. Journal of the American College of Cardiology , volume=. 2018 , publisher=

    2018

  138. [147]

    History, current status and outlook , author=

    CT calcium scoring. History, current status and outlook , author=. Diagnostic and interventional imaging , volume=. 2017 , publisher=

    2017

  139. [148]

    Missouri medicine , volume=

    Role of CT calcium scoring in screening for coronary artery disease , author=. Missouri medicine , volume=

  140. [149]

    Archives of internal medicine , volume=

    Using the coronary artery calcium score to predict coronary heart disease events: a systematic review and meta-analysis , author=. Archives of internal medicine , volume=. 2004 , publisher=

    2004

  141. [150]

    The international journal of cardiovascular imaging , volume=

    Coronary artery calcium screening: current status and recommendations from the European Society of Cardiac Radiology and North American Society for Cardiovascular Imaging , author=. The international journal of cardiovascular imaging , volume=. 2008 , publisher=

    2008

  142. [151]

    International Workshop on Statistical Atlases and Computational Models of the Heart , pages=

    Coronary Artery Calcium Scoring from Non-contrast Cardiac CT Using Deep Learning with External Validation , author=. International Workshop on Statistical Atlases and Computational Models of the Heart , pages=. 2024 , organization=

    2024

  143. [152]

    Radiology , volume=

    Increasing access to imaging for addressing the global cancer epidemic , author=. Radiology , volume=. 2021 , publisher=

    2021

  144. [153]

    Evidence Synthesis , year=

    Screening for intermediate risk factors for coronary heart disease: systematic evidence synthesis , author=. Evidence Synthesis , year=

  145. [154]

    The American Journal of Medicine , volume=

    A feasible, simple, cost-saving program to end cardiovascular disease in the United States , author=. The American Journal of Medicine , volume=. 2019 , publisher=

    2019

  146. [155]

    Circulation: Cardiovascular Imaging , volume=

    Equity in Cardiovascular Disease Prevention: A Case for Universal Coverage of Calcium Scoring , author=. Circulation: Cardiovascular Imaging , volume=. 2024 , publisher=

    2024

  147. [156]

    Radiology , volume=

    Radiologic and nuclear medicine studies in the United States and worldwide: frequency, radiation dose, and comparison with other radiation sources—1950--2007 , author=. Radiology , volume=. 2009 , publisher=

    1950

  148. [157]

    European heart journal , volume=

    The appropriate and justified use of medical radiation in cardiovascular imaging: a position document of the ESC Associations of Cardiovascular Imaging, Percutaneous Cardiovascular Interventions and Electrophysiology , author=. European heart journal , volume=. 2014 , publisher=

    2014

  149. [158]

    Journal of the American College of Cardiology , volume=

    2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines , author=. Journal of the American College of Car...

    2018

  150. [159]

    An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT) , author=

    CAC-DRS: coronary artery calcium data and reporting system. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT) , author=. Journal of cardiovascular computed tomography , volume=. 2018 , publisher=

    2018

  151. [160]

    Proceedings of the 22nd international conference on Machine learning , pages=

    Predicting good probabilities with supervised learning , author=. Proceedings of the 22nd international conference on Machine learning , pages=

  152. [161]

    Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , pages=

    Transforming classifier scores into accurate multiclass probability estimates , author=. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , pages=

  153. [162]

    Advances in neural information processing systems , volume=

    X-cal: Explicit calibration for survival analysis , author=. Advances in neural information processing systems , volume=

  154. [163]

    Journal of the American college of cardiology , volume=

    Quantification of coronary artery calcium using ultrafast computed tomography , author=. Journal of the American college of cardiology , volume=. 1990 , publisher=

    1990

  155. [164]

    JACC: Cardiovascular Imaging , volume=

    Coronary calcification and the risk of heart failure in the elderly: the Rotterdam Study , author=. JACC: Cardiovascular Imaging , volume=. 2012 , publisher=

    2012

  156. [165]

    Mayo Clinic Proceedings , volume=

    Electron beam computed tomographic coronary calcium scanning: a review and guidelines for use in asymptomatic persons , author=. Mayo Clinic Proceedings , volume=. 1999 , organization=

    1999

  157. [166]

    IEEE transactions on medical imaging , volume=

    Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions , author=. IEEE transactions on medical imaging , volume=. 2017 , publisher=

    2017

  158. [167]

    Medical Imaging 2010: Image Processing , volume=

    Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach , author=. Medical Imaging 2010: Image Processing , volume=. 2010 , organization=

    2010

  159. [168]

    Multimedia Systems , volume=

    A survey of visual neural networks: current trends, challenges and opportunities , author=. Multimedia Systems , volume=. 2023 , publisher=

    2023

  160. [169]

    Medical image computing and computer-assisted intervention--MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 , pages=

    U-net: Convolutional networks for biomedical image segmentation , author=. Medical image computing and computer-assisted intervention--MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 , pages=. 2015 , organization=

    2015

  161. [170]

    Artificial Intelligence Review , volume=

    A review of convolutional neural networks in computer vision , author=. Artificial Intelligence Review , volume=. 2024 , publisher=

    2024

  162. [171]

    International journal of computer vision , volume=

    Imagenet large scale visual recognition challenge , author=. International journal of computer vision , volume=. 2015 , publisher=

    2015

  163. [172]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Searching for mobilenetv3 , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  164. [173]

    Advances in neural information processing systems , volume=

    Coatnet: Marrying convolution and attention for all data sizes , author=. Advances in neural information processing systems , volume=

  165. [174]

    Medical physics , volume=

    Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach , author=. Medical physics , volume=. 2013 , publisher=

    2013

  166. [175]

    Artificial Intelligence Review , volume=

    Deep learning for survival analysis: a review , author=. Artificial Intelligence Review , volume=. 2024 , publisher=

    2024

  167. [176]

    NeuroImage: Clinical , volume=

    Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease , author=. NeuroImage: Clinical , volume=. 2021 , publisher=

    2021

  168. [177]

    arXiv preprint arXiv:1409.1556 , year=

    Very deep convolutional networks for large-scale image recognition , author=. arXiv preprint arXiv:1409.1556 , year=

    arXiv

  169. [178]

    Nature , volume=

    Progress and challenges in translating the biology of atherosclerosis , author=. Nature , volume=. 2011 , publisher=

    2011

  170. [179]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Deep residual learning for image recognition , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  171. [180]

    Computer Methods and Programs in Biomedicine , volume=

    ResNet and its application to medical image processing: Research progress and challenges , author=. Computer Methods and Programs in Biomedicine , volume=. 2023 , publisher=

    2023

  172. [181]

    2009 IEEE conference on computer vision and pattern recognition , pages=

    Imagenet: A large-scale hierarchical image database , author=. 2009 IEEE conference on computer vision and pattern recognition , pages=. 2009 , organization=

    2009

  173. [182]

    NeuroImage , volume=

    DAFT: A universal module to interweave tabular data and 3D images in CNNs , author=. NeuroImage , volume=. 2022 , publisher=

    2022

  174. [183]

    Netherlands Heart Journal , volume=

    Computed tomography and coronary artery calcium score for screening of coronary artery disease and cardiovascular risk management in asymptomatic individuals , author=. Netherlands Heart Journal , volume=. 2024 , publisher=

    2024

  175. [184]

    Cardiovascular imaging , volume=

    Major global coronary artery calcium guidelines , author=. Cardiovascular imaging , volume=. 2023 , publisher=

    2023

  176. [185]

    Cardiovascular Imaging , volume=

    Guideline-directed application of coronary artery calcium scores for primary prevention of atherosclerotic cardiovascular disease , author=. Cardiovascular Imaging , volume=. 2025 , publisher=

    2025

  177. [186]

    Cardiovascular Imaging , volume=

    Reply: One Score, Many Paths: Rethinking Coronary Artery Calcium Use Across Prevention Guidelines , author=. Cardiovascular Imaging , volume=. 2025 , publisher=

    2025

  178. [187]

    Bmj , volume=

    Sex differences in lifetime risk and first manifestation of cardiovascular disease: prospective population based cohort study , author=. Bmj , volume=. 2014 , publisher=

    2014

  179. [188]

    Journal of the American College of cardiology , volume=

    2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines , author=. Journal of the American College of cardiology , volume=. 2019 , publisher=

    2019

  180. [189]

    European heart journal , volume=

    2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contributi...

    2021

  181. [190]

    European heart journal , volume=

    2025 Focused Update of the 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Developed by the task force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and the European Atherosclerosis Society (EAS) , author=. European heart journa...

    2025

  182. [191]

    arXiv preprint arXiv:1904.00625 , year=

    Med3d: Transfer learning for 3d medical image analysis , author=. arXiv preprint arXiv:1904.00625 , year=

    1904 arXiv

  183. [192]

    Atherosclerosis Plus , volume=

    Coronary artery calcification score and 19 biomarkers on cardiovascular events; a 10-year follow-up DanRisk substudy , author=. Atherosclerosis Plus , volume=. 2024 , publisher=

    2024

  184. [193]

    Journal of the American College of Cardiology , volume=

    Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: a special report from the American Heart Association and American College of Cardiology , author=. Journal of the American College of Cardiology , volume=...

    2019

  185. [194]

    Design update and major findings between 2020 and 2024 , author=

    The Rotterdam Study. Design update and major findings between 2020 and 2024 , author=. European Journal of Epidemiology , pages=. 2024 , publisher=

    2020

  186. [195]

    American journal of epidemiology , volume=

    Brazilian longitudinal study of adult health (ELSA-Brasil): objectives and design , author=. American journal of epidemiology , volume=. 2012 , publisher=

    2012

  187. [196]

    NPJ digital medicine , volume=

    Automated coronary calcium scoring using deep learning with multicenter external validation , author=. NPJ digital medicine , volume=. 2021 , publisher=

    2021

  188. [197]

    IEEE transactions on medical imaging , volume=

    Direct automatic coronary calcium scoring in cardiac and chest CT , author=. IEEE transactions on medical imaging , volume=. 2019 , publisher=

    2019

  189. [198]

    Circulation: Cardiovascular Imaging , volume=

    Comparison of atherosclerotic calcification in major vessel beds on the risk of all-cause and cause-specific mortality: the Rotterdam study , author=. Circulation: Cardiovascular Imaging , volume=. 2015 , publisher=

    2015

  190. [199]

    arXiv preprint arXiv:1706.05587 , year=

    Rethinking atrous convolution for semantic image segmentation , author=. arXiv preprint arXiv:1706.05587 , year=

    arXiv

  191. [200]

    Computational and Structural Biotechnology Journal , volume=

    Privacy-preserving federated machine learning on FAIR health data: A real-world application , author=. Computational and Structural Biotechnology Journal , volume=. 2024 , publisher=

    2024

  192. [201]

    International Conference on Machine Learning, Image Processing, Network Security and Data Sciences , pages=

    Personal health train on fhir: A privacy preserving federated approach for analyzing fair data in healthcare , author=. International Conference on Machine Learning, Image Processing, Network Security and Data Sciences , pages=. 2020 , organization=

    2020

  193. [202]

    Digital Health , volume=

    Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences , author=. Digital Health , volume=. 2024 , publisher=

    2024

  194. [203]

    and Blumenthal, Roger S

    Arnett, Donna K. and Blumenthal, Roger S. and Albert, Michelle A. and Buroker, Andrew B. and Goldberger, Zachary D. and Hahn, Ellen J. and Himmelfarb, Cheryl Dennison and Khera, Amit and Lloyd-Jones, Donald and. 2019. doi:10.1161/CIR.0000000000000678 , shorttitle =

    2019 doi

  195. [204]

    doi:10.1093/eurheartj/ehab309 , shorttitle =

    doi

  196. [205]

    Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study , volume =

    Hippisley-Cox, Julia and Coupland, Carol and Brindle, Peter , urldate =. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study , volume =. doi:10.1136/bmj.j2099 , shorttitle =

    doi

Pith tools

Reviewed July 10, 2026 · model on record in the stance chip above.