Tiwari Ekta, Shrimankar Dipti, Maindarkar Mahesh, Bhagawati Mrinalini, Kaur Jiah, Singh Inder M, Mantella Laura, Johri Amer M, Khanna Narendra N, Singh Rajesh, Chaudhary Sumit, Saba Luca, Al-Maini Mustafa, Anand Vinod, Kitas George, Suri Jasjit S
Vishvswarya National Institute of Technology, Nagpur, India.
School of Bioengineering and Sciences and Research, MIT Art Design and Technology University, Pune, 4123018, India.
Rheumatol Int. 2025 Jan 2;45(1):14. doi: 10.1007/s00296-024-05756-5.
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.
女性受系统性红斑狼疮（SLE）、硬皮病、类风湿性关节炎（RA）和干燥综合征等慢性自身免疫性疾病（AD）的影响尤为严重。传统评估往往低估了患有AD的女性患心血管疾病（CVD）和中风的风险。维生素D缺乏会增加患这些疾病的易感性。AD中的CVD风险预测可受益于冠状动脉疾病（CAD）的替代生物标志物，如颈动脉超声。由于CVD风险分层的非线性，我们使用基于人工智能的系统，该系统使用AD生物标志物和颈动脉超声。研究AD与CVD/中风标志物之间的关系，包括自身抗体影响的斑块负荷。其次，研究CAD的替代生物标志物，并收集基于放射组学的特征，如颈动脉内膜中层厚度（cIMT）和斑块面积（PA）。第三也是最后一点，使用先进的机器学习（ML）和深度学习（DL）范式探索自动CVD/中风风险识别。分析了患有AD的女性的生物标志物数据，包括颈动脉超声成像、临床参数、自身抗体谱和维生素D水平。提出了人工智能（AI）模型，以准确预测患有AD的女性的CVD/中风风险。AD病程与cIMT/PA升高之间存在密切关联，CVD风险增加与类风湿因子（RF）和抗瓜氨酸化肽抗体（ACPAs）水平升高有关。通过整合成像数据和特定疾病因素，AI模型的表现优于传统方法。跨学科合作对于管理患有慢性自身免疫性疾病的女性的CVD/中风至关重要。基于AI的辅助风险分层方法可能会改善治疗决策和心血管结局。
