Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey.
Tiwari, Ekta Shrimankar, Dipti Maindarkar, Mahesh Bhagawati, Mrinalini Kaur, Jiah Singh, Inder M Mantella, Laura Johri, Amer M Khanna, Narenda N Singh, Rajesh
Tiwari, Ekta
Shrimankar, Dipti
Maindarkar, Mahesh
Bhagawati, Mrinalini
Kaur, Jiah
Singh, Inder M
Mantella, Laura
Johri, Amer M
Khanna, Narenda N
Singh, Rajesh
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Affiliation
Vishvswarya National Institute of Technology; MIT Art and Technologu University; AtheroPoint; The Dudley Group NHS Foundation Trust
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Publication date
2025-01-02
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Abstract
Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjgren'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. Competing Interests: 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. 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Citation
Tiwari E, Shrimankar D, Maindarkar M, Bhagawati M, Kaur J, Singh IM, Mantella L, Johri AM, Khanna NN, Singh R, Chaudhary S, Saba L, Al-Maini M, Anand V, Kitas G, Suri JS. Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey. Rheumatol Int. 2025 Jan 2;45(1):14. doi: 10.1007/s00296-024-05756-5