ArticleCardiovascular diabetology2022
Unsupervised machine learning based on clinical factors for the detection of coronary artery atherosclerosis in type 2 diabetes mellitus.
Article in Cardiovascular diabetology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review.Cardiovascular diabetology · 2023Pooled it
- An Unsupervised Learning Algorithm for the Automatic Classification of Coronary Artery Lesions.Cureus · 2025Article
- Predicting COPD Readmission: An Intelligent Clinical Decision Support System.Diagnostics (Basel, Switzerland) · 2025Article
- Effect of metabolic syndrome on coronary artery atherosclerotic plaque in type 2 diabetes mellitus patients.Frontiers in endocrinology · 2025Article
- Artificial intelligence in endocrinology: a comprehensive review.Journal of endocrinological investigation · 2024Review
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project.Cardiovascular diabetology · 2023Observational
- Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
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8 authors at 2 institutions in 1 country.
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Abstract
backgroundCoronary atherosclerosis can lead to serious cardiovascular events. In type 2 diabetes (T2DM) patients, the effects of clinical factors on coronary atherosclerosis have not been fully elucidated. We used a clustering method to distinguish the population heterogeneity of T2DM and the differences in coronary atherosclerosis evaluated on coronary computed tomography angiography (CCTA) among groups and to facilitate clinical management.
methodsClinical data from 1157 T2DM patients with coronary atherosclerosis who underwent CCTA in our hospital from January 2018 to September 2021 were retrospectively collected. The coronary artery segment plaque type and stenosis, the number of involved vessels, the segment involvement score (SIS) and the segment stenosis score (SSS) were evaluated and calculated. Unsupervised clustering analysis based on clinical information was used (cluster 1: n = 463; cluster 2: n = 341; cluster 3: n = 353). The association of coronary plaque characteristics with cluster groups was evaluated.
resultsThe clinical data among the three groups were different in several aspects: (1) Cluster 1 had the least male patients (41.7%), the lowest proportion of patients with smoking (0%) or alcohol history (0.9%), and the lowest level of serum creatinine (74.46 ± 22.18 µmol/L); (2) Cluster 2 had the shortest duration of diabetes (7.90 ± 8.20 years) and was less likely to be treated with diabetes (42.2%) or statins (17.6%) and (3) Cluster 3 was the youngest (65.89 ± 10.15 years old) and had the highest proportion of male patients (96.6%), the highest proportion of patients with smoking (91.2%) and alcohol (59.8%) history, the highest level of eGFR (83.81 ± 19.06 ml/min/1.73m
conclusionsThe unsupervised clustering method could address T2DM patients with heterogeneous clinical indicators and identify groups with different types of coronary plaque and degrees of coronary stenosis. This method has the potential for patient stratification, which is essential for the clinical management of T2DM patients with coronary atherosclerosis.
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