ArticleKorean journal of radiology2021
Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning.
Article in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Diagnostic Accuracy of AI Algorithms in Aortic Stenosis Screening: A Systematic Review and Meta-Analysis.Clinical medicine & research · 2024Pooled it
- Quality assessment of radiomics research in cardiac CT: a systematic review.European radiology · 2022Pooled it
- Machine learning-based early detection of abnormal heart rate in critically ill patients: a real-world clinical dataset study with automated alert system integration.Frontiers in artificial intelligence · 2026Article
- Enhanced Multi-Model Machine Learning-Based Dementia Detection Using a Data Enrichment Framework: Leveraging the Blessing of Dimensionality.Bioengineering (Basel, Switzerland) · 2025Article
- Detection of severe aortic stenosis by clinicians versus artificial intelligence: A retrospective clinical cohort study.American heart journal plus : cardiology research and practice · 2024Article
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- Aortic Valve Calcium Score by Computed Tomography as an Adjunct to Echocardiographic Assessment-A Review of Clinical Utility and Applications.Journal of imaging · 2023Review
- Enhanced detection of severe aortic stenosis via artificial intelligence: a clinical cohort study.Open heart · 2023Article
- Incremental diagnostic value of radiomics signature of pericoronary adipose tissue for detecting functional myocardial ischemia: a multicenter study.European radiology · 2023Article
- Stability selection for LASSO with weights based on AUC.Scientific reports · 2023Article
- Machine learning applications in cardiac computed tomography: a composite systematic review.European heart journal open · 2022Review
- Assessment of aortic valve area on cardiac computed tomography in symptomatic bicuspid aortic stenosis: Utility and differences from Doppler echocardiography.Frontiers in cardiovascular medicine · 2022Article
- Radiomic analysis of enhanced CMR cine images predicts left ventricular remodeling after TAVR in patients with symptomatic severe aortic stenosis.Frontiers in cardiovascular medicine · 2022Article
- Diagnostic and Prognostic Performance of Aortic Valve Calcium Score with Cardiac CT for Aortic Stenosis: A Meta-Analysis.Radiology. Cardiothoracic imaging · 2021Article
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5 authors.
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Abstract
objectiveWe aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. MATERIALS AND
methodsWe retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score.
resultsThe radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively,
conclusionModels based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.
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