Development and application of artificial intelligence in cardiac imaging

Radiomics is a process designed to extract a large number of quantitative image features using data-characterization algorithms.2,3 Radiomics allows data mining and statistical classifiers to determine the relevant features of an image to the target task and to build a prediction model, that is helpful to diagnose disorders in medical imaging. The radiomic features generally include size and shape based-features, intensity histogram, image voxel relationships, and filtered features and fractal features.4 Recently, radiomics showed to be able to differentiate hypertrophic cardiomyopathy from hypertensive heart disease; the integration of six texture and histogram features achieved an accuracy of 85.5%, outperforming the accuracy of conventional T1 weighted imaging of 64%.5 Radiomic texture analysis of late iodine enhancement on CT images reflects left ventricle remodeling and systolic–diastolic function, and may help to identify different patterns of structure remodeling.6 Coronary plaques are small and have a limited number of voxels, and are therefore very challenging for image analysis. Kolossvary et al demonstrated that the voxels of a coronary plaque were sufficient to perform a radiomic analysis, and found that 21% of radiomic parameters were significantly different between plaques with and without the napkin-ring sign and that radiomic parameters had a higher area under curve (AUC) than conventional parameters (0.92 vs 0.75).7 Kolossvary et al also performed a radiomic approach to identify advanced atherosclerotic lesions ex vivo, and showed a better AUC than visual assessment (0.73 vs 0.65).8