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Radiomics features generated from the segmented region of interest of the cardiac image. The output file of the code provides a list of 684 features which encodes two phases: end-diastolic and end-systolic information of left ventricle, right ventricle and myocardium using 114 unique radiomics plus demographic information(height,weight) plus fractals(117 unique features total). Radiomics encode relevant information present in the image by using 3 classes of features:
- First Order Features - Shape Features - Texture Features
Each class of listed features encodes certain information present in the image allowing the clinician to operate with a larger data pool rather than just an image itself.
Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound
Imaging of the heart and blood vessels is performed in a large subset of the UK Biobank cohort. Many measures defining the state of the heart and blood vessels can be derived from the images acquired. These measures are influenced by various health conditions and modifiable and non-modifiable factors, such as age, gender and ethnicity. The aim of this proposal is to describe the measures of the heart and blood vessel in the UK Biobank population and investigate how much modifiable and non-modifiable factors influence them. All new data will be made available for future research. Knowing the reference ranges for common imaging measures of the heart and circulation and how they are influenced by factors, such as age, gender, ethnicity, risk factors for heart attacks and strokes, is key for improving making diagnoses and predicting health outcomes. Descriptive statistics will be performed for all image derived phenotypes (IDPs) from the cardiovascular magnetic resonance (CMR) and carotid ultrasound images. We will perform subgroup analysis for important clinical factors, such as age, gender, cardiovascular risk, chronic conditions (e.g. Diabetes). We will apply descriptive statistics to a subpopulation considered `healthy without cardiovascular disease or presence of modifiable risk factors`. Univariate and multivariate regression analysis will be used to assess relationships between IDPs and relevant co-variates. We will also assess intra- and inter-observer variability for IDP measurement when repeat analysis is available. Initial 5000 subjects from the imaging enhancement study.