Pre-trained Artificial Intelligence Models in the Prediction and Classification of Atherosclerotic Cardiovascular Disease

Basit Öğe Kaydı
dc.contributor.authorSakiroglu, Furkan
dc.contributor.authorColak, Cemil
dc.contributor.authorColak, Mehmet Cengiz
dc.date.accessioned2026-04-04T13:30:45Z
dc.date.available2026-04-04T13:30:45Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractAtherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of global morbidity and mortality. The current study provides a systematic review of the use of artificial intelligence (AI) technologies applied to the prediction and management of ASCVD. Traditional risk assessment approaches have their restrictions, leading to a growing preference for AI and machine learning techniques in risk assessment. First, this study tackles the complex pathophysiology of ASCVD and the problems associated with the current diagnosis, followed by an in-depth analysis of the wide variety of AI models that can be applied to electronic health records, medical imaging data, and other biomarkers. Special attention will be paid toward the potential of natural language processing models like bidirectional encoder representations from transformers in predicting risk from textual clinical data, and the overwhelming success of convolutional neural networks such as residual neural network and visual geometry group in plaque-based analysis through imaging modalities. Although the research results show that these models have a lotto offer in the clinical world, the authors also describe some serious disadvantages: data bias, interpretability of the model, and computational needs. It highlights, in particular, the need for multicenter validation studies as well as developing explainable AI techniques. Overall, AI-based approaches may pave the way for a new paradigm in ASCVD management. Nevertheless, deploying these technologies in everyday clinical practice will require overcoming technical, ethical, and regulatory challenges. As such, interdisciplinary collaboration and thorough clinical validation studies are essential for fulfilling the promise of these novel strategies to enhance patient outcomes.
dc.identifier.doi10.5152/eurasianjmed.2025.25937
dc.identifier.issn1308-8742
dc.identifier.issue3
dc.identifier.pmid41669917
dc.identifier.scopus2-s2.0-105023393700
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.5152/eurasianjmed.2025.25937
dc.identifier.urihttps://hdl.handle.net/11616/108357
dc.identifier.volume57
dc.identifier.wosWOS:001685819500004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherAves
dc.relation.ispartofEurasian Journal of Medicine
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectAtherosclerotic cardiovascular
dc.subjectdeep learning
dc.subjectmachine learning
dc.subjectmedical imaging
dc.subjectpre-trained arti-ficial intelligence
dc.subjectrisk prediction
dc.titlePre-trained Artificial Intelligence Models in the Prediction and Classification of Atherosclerotic Cardiovascular Disease
dc.typeReview

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu