Skip to main content

Advertisement

SpringerLink
Log in

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

  • Review
  • Published:
Rheumatology International Aims and scope Submit manuscript

Abstract

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™–aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

ARDS:

Acute respiratory distress syndrome

ASCVD:

Atherosclerotic cardiovascular disease

ANS:

Autonomic nervous system

AUC:

Area-under-the-curve

AI:

Artificial intelligence

ACS:

Acute coronary syndrome

BMI:

Body mass index

CAD:

Coronary artery disease

CAS:

Coronary artery syndrome

CHD:

Coronary heart disease

CT:

Computed tomography

CUSIP:

Carotid ultrasound image phenotype

CV:

Cross-validation

CVD:

Cardiovascular disease

CVE:

Cardiovascular events

CNN:

Convolution neural network

DL:

Deep learning

DM:

Diabetes mellitus

DT:

Decision tree

EC:

Endothelial cell

EBBM:

Environment-based biomarkers

GT:

Ground truth

GBBM:

Genetically based biomarkers

HTN:

Hypertension

HDL:

Hybrid deep learning

ICAM:

Intercellular adhesion molecule

VCAM:

Vascular cell adhesion molecule

LBBM:

Laboratory-based biomarker

LIME:

Local interpretable model-agnostic explanations

MRI:

Magnetic resonance imaging

NR:

Not reported

NPV:

Negative predictive value

NB:

Naive Bayes

Non-ML:

Non-machine learning

OBBM:

Office-based biomarker

OH:

Orthostatic hypotension

OxLDL:

Oxidation of low-density lipoprotein

PE:

Performance evaluation

PPV:

Positive predictive value

PCA:

Principal component analysis

PBBM:

Proteomics-based biomarkers

PRISMA:

Preferred reporting items for systematic reviews and meta-analyses

PTC:

Plaque tissue characterization

RA:

Rheumatoid arthritis

RF:

Random forest

ROS:

Reactive oxides stress

RoB:

Risk of bias

ROC:

Receiver operating characteristics

RNN:

Recurrent neural network

SCORE:

Systematic coronary risk evaluation

SMOTE:

Synthetic minority over-sampling technique

SVM:

Support vector machine

SHAP:

Shapley additive explanations

TPA:

Total plaque area

TC:

Tissue characterization

US:

Ultrasound

References

  1. Jamthikar AD, Gupta D, Puvvula A et al (2020) Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging. Rheumatol Int 40:1921–1939

    Article  PubMed  PubMed Central  Google Scholar 

  2. Khanna NN, Jamthikar AD, Gupta D et al (2019) Rheumatoid arthritis: atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning–based tissue characterization. Curr Atheroscler Rep 21:1–14

    Article  Google Scholar 

  3. Kaplan MJ (2006) Cardiovascular disease in rheumatoid arthritis. Curr Opin Rheumatol 18(3):289–297

    Article  CAS  PubMed  Google Scholar 

  4. Gasparyan AY, Ayvazyan L, Blackmore H, Kitas GD (2011) Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors. Rheumatol Int 31:1409–1417

    Article  PubMed  Google Scholar 

  5. Adhikari MC, Guin A, Chakraborty S, Sinhamahapatra P, Ghosh A (2012) Subclinical atherosclerosis and endothelial dysfunction in patients with early rheumatoid arthritis as evidenced by measurement of carotid intima-media thickness and flow-mediated vasodilatation: an observational study. Seminars Arthritis Rheum 41(5):669–675

    Article  Google Scholar 

  6. van Sijl AM, Peters MJ, Knol DK et al (2011) Carotid intima media thickness in rheumatoid arthritis as compared to control subjects: a meta-analysis. Seminars Arthritis Rheum 40(5):389–397

    Article  Google Scholar 

  7. González-Gay MA, González-Juanatey C, Llorca J (2012) Carotid ultrasound in the cardiovascular risk stratification of patients with rheumatoid arthritis: when and for whom? Ann Rheum Dis 71(6):796–798

    Article  PubMed  Google Scholar 

  8. Jagpal A, Navarro-Millán I (2018) Cardiovascular co-morbidity in patients with rheumatoid arthritis: a narrative review of risk factors, cardiovascular risk assessment and treatment. BMC Rheumatol 2(1):1–14

    Article  Google Scholar 

  9. Myasoedova E, Gabriel SE (2010) Cardiovascular disease in rheumatoid arthritis: a step forward. Curr Opin Rheumatol 22(3):342–347

    Article  PubMed  Google Scholar 

  10. Konstantonis G, Singh KV, Sfikakis PP et al (2022) Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. J Rheumatol Int 42:215–239

    Article  Google Scholar 

  11. Urman A, Taklalsingh N, Sorrento C, McFarlane IM (2018) Inflammation beyond the joints: rheumatoid arthritis and cardiovascular disease. SciFed J Cardiol 2:112–121

  12. Vilne B, Ķibilds J, Siksna I, Lazda I, Valciņa O, Krūmiņa A (2022) Could artificial intelligence/machine learning and inclusion of diet-gut microbiome interactions improve disease risk prediction? Case study: coronary artery disease. Front Microbiol. https://doi.org/10.3389/fmicb.2022.627892

    Article  PubMed  PubMed Central  Google Scholar 

  13. Senn R, Elkind MS, Montaner J, Christ-Crain M, Katan M (2015) Potential role of blood biomarkers in the management of nontraumatic intracerebral hemorrhage. Cerebrovasc Dis 38(6):395–409

    Article  Google Scholar 

  14. Ding Q, Hu W, Wang R et al (2023) Signaling pathways in rheumatoid arthritis: Implications for targeted therapy. Signal Transduct Target Ther 8(1):68

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhang HG, McDermott G, Seyok T et al (2023) Identifying shared genetic architecture between rheumatoid arthritis and other conditions: a phenome-wide association study with genetic risk scores. EBioMedicine 92:104581

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Patrick MT, Nair RP, He K et al (2023) Shared genetic risk factors for MS/psoriasis suggest involvement of IL17 and JAK‐STAT signalling. Ann Neurol 13:12–20

  17. Anderson TJ, Grégoire J, Hegele RA et al (2013) 2012 update of the Canadian Cardiovascular Society guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol 29(2):151–167

    Article  PubMed  Google Scholar 

  18. Mason JC, Libby P (2015) Cardiovascular disease in patients with chronic inflammation: mechanisms underlying premature cardiovascular events in rheumatologic conditions. Eur Heart J 36(8):482–489

    Article  PubMed  Google Scholar 

  19. Nishimura K, Sugiyama D, Kogata Y et al (2007) Meta-analysis: diagnostic accuracy of anti–cyclic citrullinated peptide antibody and rheumatoid factor for rheumatoid arthritis. Ann Intern Med 146(11):797–808

    Article  PubMed  Google Scholar 

  20. Huang S-F, Chang R-F, Moon WK, Lee Y-H, Chen D-R, Suri JS (2008) Analysis of tumor vascularity using three-dimensional power Doppler ultrasound images. J IEEE Trans Med Imaging 27(3):320–330

    Article  Google Scholar 

  21. Fent GJ, Greenwood JP, Plein S, Buch MH (2017) The role of non-invasive cardiovascular imaging in the assessment of cardiovascular risk in rheumatoid arthritis: where we are and where we need to be. Ann Rheum Dis 76(7):1169–1175

    Article  PubMed  Google Scholar 

  22. Maintz D, Ozgun M, Hoffmeier A et al (2006) Selective coronary artery plaque visualization and differentiation by contrast-enhanced inversion prepared MRI. Eur Heart J 27(14):1732–1736

    Article  PubMed  Google Scholar 

  23. Saremi F, Achenbach S (2015) Coronary plaque characterization using CT. Am J Roentgenol 204(3):W249–W260

    Article  Google Scholar 

  24. Boi A, Jamthikar AD, Saba L et al (2018) A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography. J Curr Atheroscler Rep 20(7):1–17

    CAS  Google Scholar 

  25. Jamthikar AD, Khanna NN, Piga M et al (2019) Rheumatoid arthritis: its link to atherosclerosis imaging and cardiovascular risk assessment using machine-learning-based tissue characterization. Vascular and intravascular imaging trends, analysis, and challenges, volume 2: plaque characterization. IOP Publishing

    Google Scholar 

  26. Liu K, Suri JS (2005) Automatic vessel indentification for angiographic screening, ed: Google Patents

  27. Corrales A, González-Juanatey C, Peiró ME, Blanco R, Llorca J, González-Gay MA (2014) Carotid ultrasound is useful for the cardiovascular risk stratification of patients with rheumatoid arthritis: results of a population-based study. Ann Rheum Dis 73(4):722–727

    Article  PubMed  Google Scholar 

  28. Paul S, Maindarkar M, Saxena S et al (2022) Bias investigation in artificial intelligence systems for early detection of Parkinson’s disease: a narrative review. Diagn MDPI 12(1):166

    Google Scholar 

  29. Khanna NN, Maindarkar M, Saxena A et al (2022) Cardiovascular/stroke risk assessment in patients with erectile dysfunction—a role of carotid wall arterial imaging and plaque tissue characterization using artificial intelligence paradigm: a narrative review. J Diagn 12(5):1249

    CAS  Google Scholar 

  30. Suri JS, Maindarkar MA, Paul S et al (2022) Deep learning paradigm for cardiovascular disease/stroke risk stratification in Parkinson’s disease affected by COVID-19: a narrative review. J Diagn 12(7):1543

    CAS  Google Scholar 

  31. Khanna NN, Maindarkar M, Puvvula A et al (2022) Vascular implications of COVID-19: role of radiological imaging, artificial intelligence, and tissue characterization: a special report. J Cardiovasc Dev Dis 9(8):268

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Munjral S, Maindarkar M, Ahluwalia P et al (2022) Cardiovascular risk stratification in diabetic retinopathy via atherosclerotic pathway in COVID-19/non-COVID-19 frameworks using artificial intelligence paradigm: a narrative review. J Diagn 12(5):1234

    CAS  Google Scholar 

  33. Parthiban G, Srivatsa S (2012) Applying machine learning methods in diagnosing heart disease for diabetic patients. J Int J Appl Inf Syst 3(7):25–30

    Google Scholar 

  34. Oikonomou EK, Siddique M, Antoniades C (2020) Artificial intelligence in medical imaging: a radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc Res 116(13):2040–2054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Faizal ASM, Thevarajah TM, Khor SM, Chang S-W (2021) A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Comput Methods Programs Biomed 207:106190

    Article  Google Scholar 

  36. Libby P (2008) Role of inflammation in atherosclerosis associated with rheumatoid arthritis. Am J Med 121(10):S21–S31

    Article  CAS  PubMed  Google Scholar 

  37. Skeoch S, Bruce IN (2015) Atherosclerosis in rheumatoid arthritis: is it all about inflammation? Nat Rev Rheumatol 11(7):390–400

    Article  CAS  PubMed  Google Scholar 

  38. Sattar N, McCarey DW, Capell H, McInnes IB (2003) Explaining how “high-grade” systemic inflammation accelerates vascular risk in rheumatoid arthritis. Circulation 108(24):2957–2963

    Article  PubMed  Google Scholar 

  39. Kerola AM, Rollefstad S, Semb AG (2021) Atherosclerotic cardiovascular disease in rheumatoid arthritis: impact of inflammation and antirheumatic treatment. Eur Cardiol Rev. https://doi.org/10.15420/ecr.2020.44

    Article  Google Scholar 

  40. Rodríguez-Rodríguez L, López-Mejías R, García-Bermúdez M, González-Juanatey C, González-Gay MA, Martín J (2012) Genetic markers of cardiovascular disease in rheumatoid arthritis. Mediat Inflamm. https://doi.org/10.1155/2012/574817

    Article  Google Scholar 

  41. Libby P, Ridker PM, Maseri A (2002) Inflammation and atherosclerosis. Circulation 105(9):1135–1143

    Article  CAS  PubMed  Google Scholar 

  42. Libby P (2003) Vascular biology of atherosclerosis: overview and state of the art. Am J Cardiol 91(3):3–6

    Article  Google Scholar 

  43. Libby P, Clinton SK (1993) The role of macrophages in atherogenesis. Curr Opin Lipidol 4(5):355–363

    Article  CAS  Google Scholar 

  44. Fuhrman B (2012) The urokinase system in the pathogenesis of atherosclerosis. Atherosclerosis 222(1):8–14

    Article  CAS  PubMed  Google Scholar 

  45. Doran AC, Meller N, McNamara CA (2008) Role of smooth muscle cells in the initiation and early progression of atherosclerosis. Arterioscler Thromb Vasc Biol 28(5):812–819

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hansson GK, Libby P, Tabas I (2015) Inflammation and plaque vulnerability. J Intern Med 278(5):483–493

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Virmani R, Burke AP, Farb A, Kolodgie FD (2006) Pathology of the vulnerable plaque. J Am Coll Cardiol 47(8S):C13–C18

    Article  CAS  PubMed  Google Scholar 

  48. Vuilleumier N, Bratt J, Alizadeh R, Jogestrand T, Hafström I, Frostegård J (2010) Anti-apoA-1 IgG and oxidized LDL are raised in rheumatoid arthritis (RA): potential associations with cardiovascular disease and RA disease activity. Scand J Rheumatol 39(6):447–453

    Article  CAS  PubMed  Google Scholar 

  49. Maziere C, Auclair M, Maziere J-C (1994) Tumor necrosis factor enhances low density lipoprotein oxidative modification by monocytes and endothelial cells. FEBS Lett 338(1):43–46

    Article  CAS  PubMed  Google Scholar 

  50. Benincasa G, Coscioni E, Napoli C (2022) Cardiovascular risk factors and molecular routes underlying endothelial dysfunction: novel opportunities for primary prevention. Biochem Pharmacol 202:115108

    Article  CAS  PubMed  Google Scholar 

  51. Nakamura T (2011) Amyloid A amyloidosis secondary to rheumatoid arthritis: pathophysiology and treatments. Clin Exp Rheumatol 29(5):850–857

    PubMed  Google Scholar 

  52. Targońska-Stępniak B, Majdan M (2014) Serum amyloid A as a marker of persistent inflammation and an indicator of cardiovascular and renal involvement in patients with rheumatoid arthritis. Mediat Inflamm. https://doi.org/10.1155/2014/793628

    Article  Google Scholar 

  53. Roy H, Bhardwaj S, Yla-Herttuala S (2009) Molecular genetics of atherosclerosis. Hum Genet 125:467–491

    Article  CAS  PubMed  Google Scholar 

  54. Paradowska-Gorycka A, Grzybowska-Kowalczyk A, Wojtecka-Lukasik E, Maslinski S (2010) IL-23 in the pathogenesis of rheumatoid arthritis. Scand J Immunol 71(3):134–145

    Article  CAS  PubMed  Google Scholar 

  55. Kagari T, Shimozato T (2002) The importance of IL-1β and TNF-α, and the noninvolvement of IL-6, in the development of monoclonal antibody-induced arthritis. J Immunol 169(3):1459–1466

    Article  CAS  PubMed  Google Scholar 

  56. Sharma AR, Sharma G, Lee SS, Chakraborty C (2016) miRNA-regulated key components of cytokine signaling pathways and inflammation in rheumatoid arthritis. Med Res Rev 36(3):425–439

    Article  CAS  PubMed  Google Scholar 

  57. Crux NB, Elahi S (2017) Human leukocyte antigen (HLA) and immune regulation: how do classical and non-classical HLA alleles modulate immune response to human immunodeficiency virus and hepatitis C virus infections? Front Immunol 8:832

    Article  PubMed  PubMed Central  Google Scholar 

  58. Gorman JD, David-Vaudey E, Pai M, Lum RF, Criswell LA (2004) Particular HLA–DRB1 shared epitope genotypes are strongly associated with rheumatoid vasculitis. Arthritis Rheum 50(11):3476–3484

    Article  CAS  PubMed  Google Scholar 

  59. Chaudhary R, Likidlilid A, Peerapatdit T et al (2012) Apolipoprotein E gene polymorphism: effects on plasma lipids and risk of type 2 diabetes and coronary artery disease. J Cardiovasc Diabetol 11(1):1–11

    Google Scholar 

  60. Lahoz C, Schaefer EJ, Cupples LA et al (2001) Apolipoprotein E genotype and cardiovascular disease in the Framingham Heart Study. J Atherosclerosis 154(3):529–537

    Article  CAS  Google Scholar 

  61. Limonova AS, Ershova AI, Meshkov AN et al (2021) Case report: hypertriglyceridemia and premature atherosclerosis in a patient with apolipoprotein E gene ε 2 ε 1 genotype. J Front Cardiovasc Med 7:585779

    Article  Google Scholar 

  62. Tretjakovs P, Jurka A, Bormane I et al (2012) Circulating adhesion molecules, matrix metalloproteinase-9, plasminogen activator inhibitor-1, and myeloperoxidase in coronary artery disease patients with stable and unstable angina. J Clin Chim Acta 413(1–2):25–29

    Article  CAS  Google Scholar 

  63. Mohammad Beigi M, Behjati M, Mohabatkar H (2011) Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach. J Struct Funct Genom 12:191–197

    Article  CAS  Google Scholar 

  64. Suzuki A, Yamada R, Chang X et al (2003) Functional haplotypes of PADI4, encoding citrullinating enzyme peptidylarginine deiminase 4, are associated with rheumatoid arthritis. Nat Genet 34(4):395–402

    Article  CAS  PubMed  Google Scholar 

  65. Yamada R, Suzuki A, Chang X, Yamamoto K (2003) Peptidylarginine deiminase type 4: identification of a rheumatoid arthritis-susceptible gene. Trends Mol Med 9(11):503–508

    Article  CAS  PubMed  Google Scholar 

  66. He L, Kernogitski Y, Kulminskaya I et al (2016) Pleiotropic meta-analyses of longitudinal studies discover novel genetic variants associated with age-related diseases. Front Genet 7:179

    Article  PubMed  PubMed Central  Google Scholar 

  67. Karami J, Aslani S, Jamshidi A, Garshasbi M, Mahmoudi M (2019) Genetic implications in the pathogenesis of rheumatoid arthritis; an updated review. Gene 702:8–16

    Article  CAS  PubMed  Google Scholar 

  68. Rafieian-Kopaei M, Setorki M, Doudi M, Baradaran A, Nasri H (2014) Atherosclerosis: process, indicators, risk factors and new hopes. J Int J Prev Med 5(8):927

    PubMed  Google Scholar 

  69. Frostegard J, Haegerstrand A, Gidlund M, Nilsson J (1991) Biologically modified LDL increases the adhesive properties of endothelial cells. J Atheroscler 90(2–3):119–126

    Article  CAS  Google Scholar 

  70. Jamthikar AD, Gupta D, Johri AM et al (2020) Low-cost office-based cardiovascular risk stratification using machine learning and focused carotid ultrasound in an Asian-Indian cohort. J Med Syst 44(12):1–15

    Article  Google Scholar 

  71. Jamthikar A, Gupta D, Saba L et al (2020) Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: a narrative review of integrated approaches using carotid ultrasound. J Comput Biol Med 126:404–418

    Article  Google Scholar 

  72. Jamthikar AD, Gupta D, Mantella LE et al (2021) Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study. J Int J Cardiovasc Imaging 37(4):1171–1187

    Article  PubMed  Google Scholar 

  73. Biswas M, Saba L, Omerzu T et al (2021) A review on joint carotid intima-media thickness and plaque area measurement in ultrasound for cardiovascular/stroke risk monitoring: artificial intelligence framework. J Digit Imaging 34(3):581–604

    Article  PubMed  PubMed Central  Google Scholar 

  74. Jain PK, Sharma N, Saba L et al (2021) Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study. Int Angiol. https://doi.org/10.23736/S0392-9590.21.04771-4

    Article  PubMed  Google Scholar 

  75. Sanagala SS, Nicolaides A, Gupta SK et al (2021) Ten fast transfer learning models for carotid ultrasound plaque tissue characterization in augmentation framework embedded with heatmaps for stroke risk stratification. J Diagnostics 11(11):2109

    Article  Google Scholar 

  76. Jain PK, Sharma N, Saba L et al (2021) Unseen artificial intelligence—deep learning paradigm for segmentation of low atherosclerotic plaque in carotid ultrasound: a multicenter cardiovascular study. J Diagn 11(12):2257

    Google Scholar 

  77. Johri AM, Singh KV, Mantella LE et al (2022) Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. J Comput Biol Med 150:106018

    Article  Google Scholar 

  78. Araki T, Ikeda N, Shukla D et al (2016) PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: a link between carotid and coronary grayscale plaque morphology. Comput Methods Progr Biomed 128:137–158

    Article  Google Scholar 

  79. Shrivastava VK, Londhe ND, Sonawane RS, Suri JS (2015) Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm. J Expert Systems Appl 42(15–16):6184–6195

    Article  Google Scholar 

  80. Teji JS, Jain S, Gupta SK, Suri JS (2022) NeoAI 1.0: machine learning-based paradigm for prediction of neonatal and infant risk of death. J Comput Biol Med 147:105639

    Article  Google Scholar 

  81. Jamthikar A, Gupta D, Johri AM, Mantella LE, Saba L, Suri JS (2022) A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: a Canadian study. J Comput Biol Med 140:105102

    Article  Google Scholar 

  82. Acharya UR, Faust O, Alvin A et al (2013) Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. J Comput Methods Progr Biomed 110(1):66–75

    Article  Google Scholar 

  83. Maniruzzaman M, Kumar N, Abedin MM et al (2017) Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm. Comput Methods Progr Biomed 152:23–34

    Article  Google Scholar 

  84. Tseng P-Y, Chen Y-T, Wang C-H et al (2020) Prediction of the development of acute kidney injury following cardiac surgery by machine learning. J Crit Care 24(1):1–13

    Google Scholar 

  85. Ho TK (1995) Random decision forests. Proc Third Int Conf Doc Anal Recognit 1:278–282

    Article  Google Scholar 

  86. Dimitriadis SI, Liparas D, A. S. D. N. Initiative (2018) How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer’s disease: from Alzheimer’s disease neuroimaging initiative (ADNI) database. J Neural Regen Res 13(6):962

    Article  Google Scholar 

  87. Marchese Robinson RL, Palczewska A, Palczewski J, Kidley N (2017) Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets. J Chem Inf Model 57(8):1773–1792

    Article  CAS  PubMed  Google Scholar 

  88. Jamthikar A, Gupta D, Khanna NN et al (2019) A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. J Cardiovasc Diagn Therapy 9(5):420

    Article  Google Scholar 

  89. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science

    Book  Google Scholar 

  90. Durstewitz D (2017) A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements. J PLoS Comput Biol 13(6):e1005542

    Article  Google Scholar 

  91. Razaghi HS, Paninski L (2019) Filtering normalizing flows. In: Bayesian Deep Learning Workshop at NeurIPS

  92. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. J IEEE Trans Neural Netw 5(2):157–166

    Article  CAS  Google Scholar 

  93. Khanna NN, Maindarkar MA, Viswanathan V et al (2022) Cardiovascular/stroke risk stratification in diabetic foot infection patients using deep learning-based artificial intelligence: an investigative study. J Clin Med 11(22):6844

    Article  PubMed  PubMed Central  Google Scholar 

  94. Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA (2020) Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput Appl 32(20):15965–15973

    Article  Google Scholar 

  95. An Y, Tang K, Wang J (2021) Time-aware multi-type data fusion representation learning framework for risk prediction of cardiovascular diseases. IEEE/ACM Trans Comput Biol. https://doi.org/10.1109/TCBB.2021.3118418

    Article  Google Scholar 

  96. Tan L, Yu K, Bashir AK et al (2021) Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach. J Neural Comput Appl. https://doi.org/10.1007/s00521-021-06219-9

    Article  Google Scholar 

  97. Priyanga P, Pattankar VV, Sridevi S (2021) A hybrid recurrent neural network-logistic chaos-based whale optimization framework for heart disease prediction with electronic health records. J Comput Intell 37(1):315–343

    Article  Google Scholar 

  98. Kataria S, Ravindran V (2018) Digital health: a new dimension in rheumatology patient care. Rheumatol Int 38(11):1949–1957

    Article  PubMed  Google Scholar 

  99. Khanna NN, Jamthikar AD, Araki T et al (2019) Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: a Japanese diabetes cohort study. Echocardiography 36(2):345–361

    Article  PubMed  Google Scholar 

  100. Suri JS (2001) Two-dimensional fast magnetic resonance brain segmentation. IEEE Eng Med Biol Mag 20(4):84–95

    Article  CAS  PubMed  Google Scholar 

  101. Manrique de Lara A, Peláez-Ballestas I (2020) Big data and data processing in rheumatology: bioethical perspectives. Clin Rheumatol 39:1007–1014

    Article  PubMed  Google Scholar 

  102. Jamshidi A, Pelletier J-P, Martel-Pelletier J (2019) Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat Rev Rheumatol 15(1):49–60

    Article  PubMed  Google Scholar 

  103. Song Y, Bernard L, Jorgensen C, Dusfour G, Pers Y-M (2021) The challenges of telemedicine in rheumatology. Front Med 8:746219

    Article  Google Scholar 

  104. Solomon A, Tsang L, Woodiwiss AJ, Millen AM, Norton GR, Dessein PH (2014) Cardiovascular disease risk amongst African black patients with rheumatoid arthritis: the need for population specific stratification. BioMed Res Int. https://doi.org/10.1155/2014/826095

    Article  PubMed  PubMed Central  Google Scholar 

  105. Navarini L, Caso F, Costa L et al (2020) Cardiovascular risk prediction in ankylosing spondylitis: from traditional scores to machine learning assessment. Rheumatol Therapy 7:867–882

    Article  Google Scholar 

  106. McMaster C, Bird A, Liew DF et al (2022) Artificial intelligence and deep learning for rheumatologists. Arthritis Rheumatol 74(12):1893–1905

    Article  PubMed  PubMed Central  Google Scholar 

  107. Heckbert S, Lumley T, Holmes C et al (2009) Platelet count and the risk for thrombosis and death in the elderly. J Thromb Haemost 7(3):399–405

    Article  PubMed  Google Scholar 

  108. Slavka G, Perkmann T, Haslacher H et al (2011) Mean platelet volume may represent a predictive parameter for overall vascular mortality and ischemic heart disease. Arterioscler Thromb Vasc Biol 31(5):1215–1218

    Article  CAS  PubMed  Google Scholar 

  109. Blum A, Hadas V, Burke M, Yust I, Kessler A (2005) Viral load of the human immunodeficiency virus could be an independent risk factor for endothelial dysfunction. J Clin Cardiol 28(3):149–153

    Article  Google Scholar 

  110. Estévez-Loureiro R, Salgado-Fernández J, Marzoa-Rivas R et al (2009) Mean platelet volume predicts patency of the infarct-related artery before mechanical reperfusion and short-term mortality in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Thromb Res 124(5):536–540

    Article  PubMed  Google Scholar 

  111. van Dijk RA, Rauwerda JA, Steyn M, Twisk JW, Stehouwer CD (2001) Long-term homocysteine-lowering treatment with folic acid plus pyridoxine is associated with decreased blood pressure but not with improved brachial artery endothelium-dependent vasodilation or carotid artery stiffness: a 2-year, randomized, placebo-controlled trial. J Arterioscler Thromb Vasc Biol 21(12):2072–2079

    Article  Google Scholar 

  112. Aziz H, Zaas A, Ginsburg GS (2007) Peripheral blood gene expression profiling for cardiovascular disease assessment. Genom Med 1(3):105–112

    Article  Google Scholar 

  113. Pordzik J, Pisarz K, De Rosa S et al (2018) The potential role of platelet-related microRNAs in the development of cardiovascular events in high-risk populations, including diabetic patients: a review. Front Endocrinol 9:74

    Article  Google Scholar 

  114. Casas JP, Cavalleri GL, Bautista LE, Smeeth L, Humphries SE, Hingorani AD (2006) Endothelial nitric oxide synthase gene polymorphisms and cardiovascular disease: a HuGE review. J Am J Epidemiol 164(10):921–935

    Article  PubMed  Google Scholar 

  115. Reddy VH (2014) Automatic red blood cell and white blood cell counting for telemedicine system. Int J Res Advent Technol 2(1):203–10

  116. Barany F (1991) Genetic disease detection and DNA amplification using cloned thermostable ligase. Proc Natl Acad Sci 88(1):189–193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Kim K-J, Tagkopoulos I (2019) Application of machine learning in rheumatic disease research. Korean J Intern Med 34(4):708

    Article  PubMed  PubMed Central  Google Scholar 

  118. Stoel B (2020) Use of artificial intelligence in imaging in rheumatology–current status and future perspectives. RMD Open 6(1):e001063

    Article  PubMed  PubMed Central  Google Scholar 

  119. Hügle M, Omoumi P, van Laar JM, Boedecker J, Hügle T (2020) Applied machine learning and artificial intelligence in rheumatology. Rheumatol Adv Pract 4(1):rkaa005

    Article  PubMed  PubMed Central  Google Scholar 

  120. Gruson D, Bernardini S, Dabla PK, Gouget B, Stankovic S (2020) Collaborative AI and laboratory medicine integration in precision cardiovascular medicine. J Clin Chim Acta 509:67–71

    Article  CAS  Google Scholar 

  121. Lippi G, Plebani M (2014) Red blood cell distribution width (RDW) and human pathology. One size fits all. Clin Chem Lab Med (CCLM) 52(9):1247–1249

    Article  CAS  PubMed  Google Scholar 

  122. Gasparyan AY, Ayvazyan L, Mukanova U, Yessirkepov M, Kitas GD (2019) The platelet-to-lymphocyte ratio as an inflammatory marker in rheumatic diseases. Ann Lab Med 39(4):345

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Pai JK, Cahill LE, Hu FB, Rexrode KM, Manson JE, Rimm EB (2013) Hemoglobin a1c is associated with increased risk of incident coronary heart disease among apparently healthy, nondiabetic men and women. J Am Heart Assoc 2(2):e000077

    Article  PubMed  PubMed Central  Google Scholar 

  124. Saltzman JR, Tabak YP, Hyett BH, Sun X, Travis AC, Johannes RS (2011) A simple risk score accurately predicts in-hospital mortality, length of stay, and cost in acute upper GI bleeding. Gastrointest Endosc 74(6):1215–1224

    Article  PubMed  Google Scholar 

  125. Khaw K-T, Dowsett M, Folkerd E et al (2007) Endogenous testosterone and mortality due to all causes, cardiovascular disease, and cancer in men: European prospective investigation into cancer in Norfolk (EPIC-Norfolk) Prospective Population Study. J Circ 116(23):2694–2701

    Article  CAS  Google Scholar 

  126. Gasparyan AY, Stavropoulos-Kalinoglou A, Mikhailidis DP, Douglas KM, Kitas GD (2011) Platelet function in rheumatoid arthritis: arthritic and cardiovascular implications. Rheumatol Int 31:153–164

    Article  CAS  PubMed  Google Scholar 

  127. Bhat T, Teli S, Rijal J et al (2013) Neutrophil to lymphocyte ratio and cardiovascular diseases: a review. Expert Rev Cardiovasc Ther 11(1):55–59

    Article  CAS  PubMed  Google Scholar 

  128. Acharya U, Vinitha Sree S, Mookiah M et al (2013) Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classification. J Proc Inst Mech Eng Part H 227(7):788–798

    Article  CAS  Google Scholar 

  129. Vlachopoulos C, Aznaouridis K, Ioakeimidis N et al (2006) Unfavourable endothelial and inflammatory state in erectile dysfunction patients with or without coronary artery disease. J Eur Heart J 27(22):2640–2648

    Article  CAS  PubMed  Google Scholar 

  130. Gandaglia G, Briganti A, Jackson G et al (2014) A systematic review of the association between erectile dysfunction and cardiovascular disease. J Eur Urol 65(5):968–978

    Article  Google Scholar 

  131. Suri J, Agarwal S, Gupta S et al (2021) Systematic review of artificial intelligence in acute respiratory distress syndrome for COVID-19 lung patients: a biomedical imaging perspective. IEEE J Biomed Health Inform 25(11):4128–4139. https://doi.org/10.1109/JBHI.2021.3103839

    Article  PubMed  Google Scholar 

  132. Suri JS, Bhagawati M, Paul S et al (2022) Understanding the bias in machine learning systems for cardiovascular disease risk assessment: the first of its kind review. Comput Biol Med 142:142–159

    Article  Google Scholar 

  133. Suri JS, Agarwal S, Jena B et al (2022) Five strategies for bias estimation in artificial intelligence-based hybrid deep learning for acute respiratory distress syndrome COVID-19 lung infected patients using AP(ai)Bias 2.0: a systematic review. IEEE TIM. https://doi.org/10.1109/TIM.2022.3174270

    Article  Google Scholar 

  134. Suri JS, Bhagawati M, Paul S et al (2022) A powerful paradigm for cardiovascular risk stratification using multiclass, multi-label, and ensemble-based machine learning paradigms: a narrative review. J Diagn 12(3):722

    Google Scholar 

  135. Lee S, Joo Y, Kim W et al (2001) Vascular endothelial growth factor levels in the serum and synovial fluid of patients with rheumatoid arthritis. Clin Exp Rheumatol 19(3):321–324

    CAS  PubMed  Google Scholar 

  136. Kellesarian SV, Al-Kheraif AA, Vohra F et al (2016) Cytokine profile in the synovial fluid of patients with temporomandibular joint disorders: a systematic review. Cytokine 77:98–106

    Article  CAS  PubMed  Google Scholar 

  137. Cope AP, Gibbons D, Brennan FM et al (1992) Increased levels of soluble tumor necrosis factor receptors in the sera and synovial fluid of patients with rheumatic diseases. Arthritis Rheum 35(10):1160–1169

    Article  CAS  PubMed  Google Scholar 

  138. Kaneko S, Satoh T, Chiba J, Ju C, Inoue K, Kagawa J (2000) Interleukin–6 and interleukin–8 levels in serum and synovial fluid of patients with osteoarthritis. Cytokines Cell Mol Ther 6(2):71–79

    Article  CAS  PubMed  Google Scholar 

  139. Kisacik B, Tufan A, Kalyoncu U et al (2008) Mean platelet volume (MPV) as an inflammatory marker in ankylosing spondylitis and rheumatoid arthritis. Jt Bone Spine 75(3):291–294

    Article  Google Scholar 

  140. Presle N, Pottie P, Dumond H et al (2006) Differential distribution of adipokines between serum and synovial fluid in patients with osteoarthritis. Contribution of joint tissues to their articular production. Osteoarthr Cartil 14(7):690–695

    Article  CAS  Google Scholar 

  141. Bhuanantanondh P, Grecov D, Kwok E (2010) Rheological study of viscosupplements and synovial fluid in patients with osteoarthritis. CMBES Proc 33:1–4

  142. Ostalowska A, Birkner E, Wiecha M et al (2006) Lipid peroxidation and antioxidant enzymes in synovial fluid of patients with primary and secondary osteoarthritis of the knee joint. Osteoarthr Cartil 14(2):139–145

    Article  CAS  Google Scholar 

  143. Panwar A, Semwal G, Goel S, Gupta S (2022) Stratification of the lesions in color fundus images of diabetic retinopathy patients using deep learning models and machine learning classifiers. Edge analytics. Springer, pp 653–666

    Chapter  Google Scholar 

  144. Zhu M, Gupta S (2017) To prune, or not to prune: exploring the efficacy of pruning for model compression. J Arxiv arXiv:1710.01878.

  145. Agarwal M, Agarwal S, Saba L et al (2022) Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: a multicenter study using COVLIAS 2.0. J Comput Biol Med 143:1571

    Google Scholar 

  146. Agarwal M, Agarwal S, Saba L et al (2022) Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: a multicenter study using COVLIAS 2.0. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2022.105571

    Article  PubMed  PubMed Central  Google Scholar 

  147. Acharya UR, Mookiah MRK, Sree SV et al (2014) Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification. Eur J Ultrasound 35(03):237–245

    CAS  Google Scholar 

  148. Xuan J, Jiang H, Hu Y et al (2014) Towards effective bug triage with software data reduction techniques. J IEEE Trans Knowl Data Eng 27(1):264–280

    Article  Google Scholar 

  149. Slack D, Hilgard S, Jia E, Singh S, Lakkaraju H (2020) Fooling lime and shap: adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp 180–186

  150. Biswas M, Kuppili V, Saba L et al (2019) State-of-the-art review on deep learning in medical imaging. Front Biosci (Landmark Ed) 24:392–426

    Article  PubMed  Google Scholar 

  151. Jena B, Saxena S, Nayak GK et al (2022) Brain tumor characterization using radiogenomics in artificial intelligence framework. J Cancers 14(16):4052

    Article  CAS  Google Scholar 

  152. Khanna NN, Maindarkar MA, Viswanathan V et al (2022) Economics of artificial intelligence in healthcare: diagnosis vs treatment. Healthcare 10(12):2493

    Article  PubMed  PubMed Central  Google Scholar 

  153. El-Baz A, Gimelfarb G, Suri JS (2015) Stochastic modeling for medical image analysis. CRC Press

    Book  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada

    Mustafa Al-Maini

  2. Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA

    Mahesh Maindarkar, Inder M. Singh & Jasjit S. Suri

  3. Asia Pacific Vascular Society, New Delhi, 110001, India

    Mahesh Maindarkar & Narendra N. Khanna

  4. Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, DY1 2HQ, UK

    George D. Kitas

  5. Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13 9PL, UK

    George D. Kitas

  6. Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India

    Narendra N. Khanna

  7. Department of Immunology, SGPIMS, Lucknow, 226014, India

    Durga Prasanna Misra, Vikas Agarwal & Aman Sharma

  8. Division of Cardiology, Department of Medicine, Queen’s University, Kingston, Canada

    Amer M. Johri

  9. Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada

    Laura Mantella

  10. Department of Surgery, Aristoteleion University of Thessaloniki, 54124, Thessaloniki, Greece

    George Tsoulfas

  11. Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA

    John R. Laird

  12. Department of Pathology, Azienda Ospedaliero Universitaria, 09124, Cagliari, Italy

    Gavino Faa

  13. Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, 60611, USA

    Jagjit Teji

  14. The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753, Delmenhorst, Germany

    Monika Turk

  15. Department of Radiology and Ultrasound, UHID, 10 000, Zagreb, Croatia

    Klaudija Viskovic

  16. Invasive Cardiology Division, University of Szeged, Szeged, Hungary

    Zoltan Ruzsa

  17. Cardiology Clinic, Onassis Cardiac Surgery Centre, Athens, Greece

    Sophie Mavrogeni

  18. Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA

    Vijay Rathore

  19. Men’s Health Centre, Miriam Hospital Providence, Providence, RI, 02906, USA

    Martin Miner

  20. Department of Radiology, Harvard Medical School, Boston, MA, USA

    Manudeep K. Kalra

  21. Department of Radiobiology and Molecular Genetics, National Institute of the Republic of Serbia, University of Belgrade, 11000, Belgrade, Serbia

    Esma R. Isenovic

  22. Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy

    Luca Saba

  23. Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA

    Mostafa M. Fouda

Authors
  1. Mustafa Al-Maini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahesh Maindarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. George D. Kitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Narendra N. Khanna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Durga Prasanna Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amer M. Johri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Laura Mantella
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vikas Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aman Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Inder M. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. George Tsoulfas
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. John R. Laird
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gavino Faa
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jagjit Teji
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Monika Turk
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Klaudija Viskovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Zoltan Ruzsa
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Sophie Mavrogeni
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Vijay Rathore
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Martin Miner
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Manudeep K. Kalra
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Esma R. Isenovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Luca Saba
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Mostafa M. Fouda
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Jasjit S. Suri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MM: design of the manuscript, proofreading many iterations, researching PubMed and other research sites for article search. MAM, GDK, NNK, MM, DPM: resources, imaging contribution and proofreading the manuscript. MM, AMJ, LM, ESR: design of the rheumatoid arthritis component of the manuscript, proofreading many iterations, researching PubMed and other research sites for article search. JSS, VA, AS, GT: proofreading and guidance of cardiology components of the manuscript. JSS, IMS, NNK: the vision of cardiac risk assessment and proofreading the manuscript, final approval of the manuscript. MKK, LS, MM: design and support of radiology components such as CT and carotid ultrasound. JRL, GF, JT: proofreading and guidance of cardiology imaging components of the manuscript. JRL, MT, KV, ZR: proofreading and guidance of cardiology and vascular components. GDK, MAM: design and solid proofreading of the manuscript, especially the rheumatology component, revising it critically for important intellectual content, and final approval of the manuscript. SM, VR, MKK ESR, MMF: vascular tissue characterization and proofreading of the manuscript. JSS: principal investigator-design, proofreading of the manuscript and management.

Corresponding author

Correspondence to Jasjit S. Suri.

Ethics declarations

Conflict of interest

All authors are full-time employees at their indicated affiliation institutions, which are public universities and hospitals. None of the authors received fees, bonuses or other benefits for the work described in the manuscript.

Ethical approval

Ethical clearance was obtained from the ethical committee of the University of Tours. All participants provided written informed consent for data collection and publication prior to data collection. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. We acknowledge that the manuscript was written and edited by authors only.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Maini, M., Maindarkar, M., Kitas, G.D. et al. Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review. Rheumatol Int 43, 1965–1982 (2023). https://doi.org/10.1007/s00296-023-05415-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00296-023-05415-1

Keywords

Search

Navigation