… regions, and multiple machine learning algorithms were used for radiomic signature (Rad-sig) …
when CMR is unavailable. Hence, we aimed to investigate the ability of radiomic features …

… CMR has emerged as a valuable tool to assess LV … by ConQuaFea and radiomic features
using machine learning and … by machine learning approaches as well as radiomic features. In …

… radiomic studies focusing on the diagnosis and differentiation of HCM by CMR, this study
emphasized the utility of radiomics … prediction in HCM patients when the CMR is unavailable. …

… set up a pipeline using radiomics feature extraction and machine learning to predict high-…
participants undergoing CMR. We demonstrate for the first time that PAT radiomics can be used …

… With the use of machine learning and simple and higher order statistics, these radiomic …
The potential of cine CMR radiomics was demonstrated in several studies that have suggested …

… Recently, the analysis of CMR scans with radiomics … strain, and short axis OS-CMR
images were acquired (total image … including an advanced machine learning algorithm (cvi42™ …

… traditional machine learning (ML) and deep learning … radiomics workflow is image acquisition
and reconstruction. In cardiac imaging, most radiomics analyses are based on CT and CMR…

… We applied radiomics analysis to CMR data from 2 vendors using different … machine
learning or deep learning approaches for integrating the clinical risk factors and the radiomic …

… ; thus, the ML-enabled automation of CMR image analysis would be desirable. Motivated
by this, … in noncontrast cine CMR images of patients with hypertrophic cardiomyopathy (HCM). …

… Previous AI-based cine-CMR research was mainly focused on the application of using
radiomics analysis which had its intrinsic limitations, namely being time-consuming, less …