Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment. Traditional diagnosis methods are expensive, labor-intensive and require clinical expertise, posing a barrier to screening for MR. To overcome this impediment, we propose a new semi-supervised model for MR classification called CUSSP. CUSSP operates on cardiac imaging slices of the 4-chamber view of the heart. It uses standard computer vision techniques and contrastive models to learn from large amounts of unlabeled data, in conjunction with specialized classifiers to establish the first ever automated MR classification system. Evaluated on a test set of 179 labeled -- 154 non-MR and 25 MR -- sequences, CUSSP attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the first benchmark result for this new task.

Artificial intelligence's (AI) applicability in cardiac imaging is rapidly growing and was a major topic of discussion at this year's EuroPCR 2022 meeting. Many session speakers discussed how they are using AI tools in their day-to-day practice and in their research to improve decision-making and patient/research outcomes. It's no secret, however, that AI tools are only as good as the data sets and the thousands of expert opinions used to power them. Implementing AI applications in our day-to-day practice, from an operations standpoint, could mean adjusting clinician workflows and setting aside time to set up and train on the new systems. And from an efficacy standpoint, it leaves clinicians wary of result accuracy, especially if they are unsure how good the data used to power the technology really is.

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

The field of nuclear cardiology has evolved over the last several decades. The field has advanced from basic first-pass radionuclide ventriculography to gated myocardial perfusion imaging with single-photon emission computed tomography (SPECT) with solid state cadmium zinc telluride cameras. There has also been adjunctive use of computed tomography (CT) technology for attenuation correction with the benefit of utilizing the transmission CT image to obtain additional information on the presence or absence of coronary calcification for additional prognostic information. Additionally, there has also been development of gated myocardial perfusion acquisition with positron emission tomography (PET) and use of CT transmission imaging for attenuation correction as well. The utilization of PET cardiac imaging has led to the ability to assess myocardial blood flow, thus improving sensitivity and specificity of myocardial PET imaging.