ArticleCardiovascular diagnosis and therapy2019
A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes.
Article in Cardiovascular diagnosis and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- Artificial intelligence in carotid research: a 25-year bibliometric analysis of global trends and future directions.Frontiers in artificial intelligence · 2026Pooled it
- Plaque-level machine-learning prediction of carotid plaque vulnerability on computed tomography angiography.Neuroradiology · 2026Article
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Optimizing Stroke Risk Prediction: A Primary Dataset-Driven Ensemble Classifier With Explainable Artificial Intelligence.Health science reports · 2025Article
- Transformer and Attention-Based Architectures for Segmentation of Coronary Arterial Walls in Intravascular Ultrasound: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction inFrontiers in genetics · 2025Article
- Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.Health information science and systems · 2024Article
- Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.Diagnostics (Basel, Switzerland) · 2024Article
- Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice.Healthcare (Basel, Switzerland) · 2024Review
- A machine learning model predicts stroke associated with blood cadmium level.Scientific reports · 2024Article
- Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.The international journal of cardiovascular imaging · 2024Article
- UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Symmetry and asymmetry in atherosclerosis.International journal of occupational medicine and environmental health · 2023Review
- Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.Journal of cardiovascular development and disease · 2023Review
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning and the Conundrum of Stroke Risk Prediction.Arrhythmia & electrophysiology review · 2023Review
- Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
20 authors at 12 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.
methodsThe ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.
resultsLeft and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72).
conclusionsML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.