ArticleFrontiers in cardiovascular medicine2020
Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs.
Article in Frontiers in cardiovascular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Novel Neighborhood Rough Set-Based Feature Selection Method and Its Application to Biomarker Identification of Schizophrenia.IEEE journal of biomedical and health informatics · 2023Article
- Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging.Frontiers in cardiovascular medicine · 2022Review
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Authors and funding
5 authors.
Funding
Abstract
We perform unsupervised analysis of image-derived shape and motion features extracted from 3,822 cardiac Magnetic resonance imaging (MRIs) of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify 2 small clusters that probably correspond to 2 pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this finding. Moreover, we examine the differences between the other large clusters and compare our measures with the ground truth.
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