ArticleScientific reports2021
Machine learning models for screening carotid atherosclerosis in asymptomatic adults.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 18 citations in OpenAlex.
- Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia Population.Vascular health and risk management · 2024Trial
- Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease.Renal failure · 2023Trial
- Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.Scientific reports · 2026Article
- Risk stratification in intermediate asymptomatic carotid artery disease: a multi-modal approach combining local flow dynamics and systemic clinical profiles.Frontiers in surgery · 2026Article
- Hunting for a coronary artery disease diagnosis in asymptomatic patients with diabetes mellitus: if, how and when.Cardiovascular diabetology · 2025Review
- Choroidal thickness as predictor of subclinical carotid atherosclerosis in adults with type 1 diabetes.Scientific reports · 2025Article
- Establishing a novel model to assess exercise capacity in chronic heart failure based on stress echocardiography.ESC heart failure · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- A deep learning algorithm to identify carotid plaques and assess their stability.Frontiers in artificial intelligence · 2024Article
- Development and validation of explainable machine-learning models for carotid atherosclerosis early screening.Journal of translational medicine · 2023Article
- Early prediction of atherosclerosis diagnosis with medical ambient intelligence.Frontiers in physiology · 2023Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Carotid atherosclerosis (CAS) is a risk factor for cardiovascular and cerebrovascular events, but duplex ultrasonography isn't recommended in routine screening for asymptomatic populations according to medical guidelines. We aim to develop machine learning models to screen CAS in asymptomatic adults. A total of 2732 asymptomatic subjects for routine physical examination in our hospital were included in the study. We developed machine learning models to classify subjects with or without CAS using decision tree, random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP) with 17 candidate features. The performance of models was assessed on the testing dataset. The model using MLP achieved the highest accuracy (0.748), positive predictive value (0.743), F1 score (0.742), area under receiver operating characteristic curve (AUC) (0.766) and Kappa score (0.445) among all classifiers. It's followed by models using XGBoost and SVM. In conclusion, the model using MLP is the best one to screen CAS in asymptomatic adults based on the results from routine physical examination, followed by using XGBoost and SVM. Those models may provide an effective and applicable method for physician and primary care doctors to screen asymptomatic CAS without risk factors in general population, and improve risk predictions and preventions of cardiovascular and cerebrovascular events in asymptomatic adults.
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