Trends and Advancements in Atherosclerosis Diagnosis and Therapy: A Future towards Predictive and Personalized Medicine
Sataditya Jana
, Aditi Kulshreshtha , Aditi Singh , Kamel Haydar , Azhar Khan
Atherosclerosis; Early detection; Diagnostic techniques; Imaging modalities; Biomarkers; Artificial intelligence; Health literacy
Atherosclerosis remains the leading cause of ischemic heart disease and stroke, responsible for over 30 million cases annually and a significant global burden of Disability-Adjusted Life Years (DALYs). Despite advances in diagnostic technologies, current methods often fail to detect atherosclerosis in its silent or early stages, limiting opportunities for timely intervention. This review provides a comprehensive overview of traditional and modern diagnostic approaches, including physical examinations, imaging modalities, biomarker profiling, functional tests, and emerging artificial intelligence (AI) and machine learning (ML) applications. While conventional tools such as angiography, ultrasound, MRI, ECG, and treadmill testing continue to play critical roles, they suffer from limitations in sensitivity, specificity, and predictive accuracy. Biomarkers offer additional insights but lack reliability when used in isolation. Recent innovations in AI-driven imaging analysis, risk modeling, and biomarker integration demonstrate strong potential to address these diagnostic gaps, with convolutional neural networks, ensemble learning, and hybrid models achieving accuracies surpassing traditional methods. We emphasize the urgent need to combine multi-modal diagnostics with AI-based probabilistic models and reinforce health literacy through digital platforms. By integrating these strategies, the future of atherosclerosis management may shift from late-stage detection to proactive, personalized, and predictive prevention.
"Trends and Advancements in Atherosclerosis Diagnosis and Therapy: A Future towards Predictive and Personalized Medicine", JNRID - JOURNAL OF NOVEL RESEARCH AND INNOVATIVE DEVELOPMENT (www.JNRID.org), ISSN:2984-8687, Vol.3, Issue 10, page no.a74-a90, October-2025, Available :https://tijer.org/JNRID/papers/JNRID2510012.pdf
Volume 3
Issue 10,
October-2025
Pages : a74-a90
Paper Reg. ID: JNRID_701590
Published Paper Id: JNRID2510012
Downloads: 000243
Research Area: Health Science All
Country: Solan, Himachal Pradesh, India
ISSN: 2984-8687 | IMPACT FACTOR: 9.57 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: JNRID (IJ Publication) Janvi Wave
