Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 Jan 2;45(1):14.
doi: 10.1007/s00296-024-05756-5.

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey

Ekta Tiwari  1 , Dipti Shrimankar  1 , Mahesh Maindarkar  2   3 , Mrinalini Bhagawati  4 , Jiah Kaur  3 , Inder M Singh  3 , Laura Mantella  5 , Amer M Johri  6 , Narendra N Khanna  7 , Rajesh Singh  8 , Sumit Chaudhary  8 , Luca Saba  9 , Mustafa Al-Maini  5 , Vinod Anand  3 , George Kitas  10   11 , Jasjit S Suri  12   13   14   15
Affiliations
Review

Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey

Ekta Tiwari et al. Rheumatol Int. .

Abstract

Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascular disease (CVD) and stroke risk in women having AD. Vitamin D deficiency increases susceptibility to these conditions. CVD risk prediction in AD can benefit from surrogate biomarker for coronary artery disease (CAD), such as carotid ultrasound. Due to non-linearity in the CVD risk stratification, we use artificial intelligence-based system using AD biomarkers and carotid ultrasound. Investigate the relationship between AD and CVD/stroke markers including autoantibody-influenced plaque load. Second, to study the surrogate biomarkers for the CAD and gather radiomics-based features such as carotid intima-media thickness (cIMT), and plaque area (PA). Third and final, explore the automated CVD/stroke risk identification using advanced machine learning (ML) and deep learning (DL) paradigms. Analysed biomarker data from women with AD, including carotid ultrasonography imaging, clinical parameters, autoantibody profiles, and vitamin D levels. Proposed artificial intelligence (AI) models to predict CVD/stroke risk accurately in AD for women. There is a strong association between AD duration and elevated cIMT/PA, with increased CVD risk linked to higher rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPAs) levels. AI models outperformed conventional methods by integrating imaging data and disorder-specific factors. Interdisciplinary collaboration is crucial for managing CVD/stroke in women with chronic autoimmune diseases. AI-based assisted risk stratification methods may improve treatment decision-making and cardiovascular outcomes.

Keywords: Artificial intelligence autoimmune disorder; Carotid ultrasound; Deep learning; Machine learning; Vitamin D deficiency.

PubMed Disclaimer

Conflict of interest statement

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.

References

    1. Abrarovna GN, Zakirovna RM, Abdumalikovna ND, Abrarovna AN (2021) Clinic-diagnostic aspects of modern biomarkers of early atherosclerosis and fibrotic activity of systemic scleroderma
    1. Acharya UR, Faust O, Alvin A, Krishnamurthi G, Seabra JC, Sanches J, Suri JS (2013a) Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. J Comput Methods Progr Biomed 110(1):66–75 - DOI
    1. Acharya UR, Faust OS, Vinitha Molinari S, Filippo Garberoglio R, Suri JS (2011) Cost-effective and non-invasive automated benign & malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: a class of ThyroScan™ algorithms. J Technol Cancer Res Treat 10(4):371–380 - DOI
    1. Acharya UR, Mookiah MRK, Sree SV, Afonso D, Sanches J, Shafique S et al (2013) Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput 51(5):513–523 - PubMed - DOI
    1. Acharya UR, Mookiah MRK, Sree SV, Yanti R, Martis R, Saba L et al (2014) Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification. J Ultraschall in der Medizin-Eur J Ultrasound 35(03):237–245

MeSH terms

LinkOut - more resources