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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Imaging Modalities in Assessing Cardiovascular Risk Factors |
| Authors Name: | Manish Kashyap , Taufique Ansari , Anjali kumari , Sweeti kumari , Chandan Kumar |
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| Author Reg. ID: |
IJRTI_205398
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| Published Paper Id: | IJRTI2507101 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | |
| Abstract: | Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide. Traditional risk assessment models, while valuable, often fall short in detecting subclinical disease. Advances in imaging modalities offer enhanced capabilities for early detection and risk stratification. This review synthesizes current imaging techniques utilized in assessing cardiovascular risk factors, highlighting their unique contributions and clinical potential. Coronary computed tomography (CT) enables coronary artery calcium (CAC) scoring, providing a direct measure of atherosclerotic burden and serving as an independent predictor of cardiac events. Coronary CT angiography (CCTA) offers non-invasive visualization of coronary vasculature, detecting stenosis and high-risk plaque features. Magnetic resonance imaging (MRI), particularly cardiac MRI (CMR), assesses myocardial structure, fibrosis, and functional markers such as ascending aortic distensibility, emerging as a novel indicator of vascular aging and major adverse cardiovascular event (MACE) risk. Echocardiography remains a first-line, cost-effective imaging technique, evaluating ventricular morphology, ejection fraction, diastolic function, valve pathology, and carotid intima-media thickness. Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) provide high-resolution imaging of arterial walls and plaque components, offering critical insights into vulnerable plaque characteristics. Retinal imaging, including OCT and fundus photography, combined with deep learning algorithms, has demonstrated potential in predicting future CVD events. Models trained on large datasets have achieved area under the curve (AUC) values of approximately 0.75 to 0.78, outperforming traditional risk scores like QRISK3. Integration of multimodal imaging and artificial intelligence (AI) enhances risk stratification, enabling personalized preventive strategies. However, challenges such as ionizing radiation exposure, standardization across technologies, and AI interpretability must be addressed to translate these advancements into clinical practice effectively. |
| Keywords: | Cardiovascular disease, Risk assessment, Imaging modalities, Cardiac MRI, Echocardiography, Subclinical atherosclerosis, Echocardiography |
| Cite Article: | "Imaging Modalities in Assessing Cardiovascular Risk Factors", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.b1-b4, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507101.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2507101
Registration ID:205398
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: b1-b4 Country: New Delhi, Delhi, India Research Area: Medical Science Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507101 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507101 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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