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Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a
Ekta Tiwari1, Dipti Shrimankar1, Mahesh Maindarkar2,3
1Vishvswarya National Institute of Technology, Nagpur, India.
Insights
Women with autoimmune diseases (AD) face underestimated cardiovascular disease (CVD) and stroke risks. Artificial intelligence models integrating biomarkers and carotid ultrasound improve risk prediction, aiding timely interventions.
Area of Science:
- Cardiovascular disease research
- Autoimmune disease management
- Medical artificial intelligence
Background:
- Women with chronic autoimmune diseases (AD) experience disproportionately high cardiovascular disease (CVD) and stroke risks, often underestimated by traditional assessments.
- Vitamin D deficiency is linked to increased susceptibility to AD and associated cardiovascular complications.
- Current CVD risk stratification in AD lacks precision, necessitating advanced methods for accurate assessment.
Purpose of the Study:
- To investigate the relationship between AD and CVD/stroke markers, including autoantibody-influenced plaque burden.
- To identify surrogate biomarkers for coronary artery disease (CAD) using radiomics features like carotid intima-media thickness (cIMT) and plaque area (PA).
- To develop and validate automated CVD/stroke risk identification models using machine learning (ML) and deep learning (DL) in women with AD.
Main Methods:
- Analysis of biomarker data from women with AD, encompassing carotid ultrasonography, clinical parameters, autoantibody profiles, and vitamin D levels.
- Development of artificial intelligence (AI) models to integrate diverse data for precise CVD/stroke risk prediction.
- Utilizing radiomics features (cIMT, PA) and autoantibody levels (RF, ACPAs) within AI frameworks.
Main Results:
- A significant association was found between AD duration and elevated cIMT/PA, indicating increased CVD risk.
- Higher levels of rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPAs) correlated with heightened CVD risk.
- AI models demonstrated superior performance over conventional methods by effectively integrating imaging data and AD-specific factors.
Conclusions:
- AI-driven risk stratification shows promise for improving cardiovascular outcomes in women with chronic autoimmune diseases.
- Interdisciplinary collaboration is essential for comprehensive management of CVD/stroke risks in this patient population.
- AI-assisted risk assessment can enhance treatment decisions and patient management strategies.
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.
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