AI-based techniques in the field of cardiac imaging | Dall - e 2 Github | Best Dall e Free Alternative Reddit | Ai art Generator Free Online | Turtles AI

  A scientific article titled "Artificial intelligence in cardiac imaging: where we are and what we want" has recently appeared in the "European Heart Journal", about the use of AI-based techniques in the field of cardiac imaging. These have the chance of revolutionizing every aspect of the process from study indication to patient-specific risk prediction and prognostication. With over 300 AI-enabled devices already approved by the Food and Drug Administration (FDA), this technology is rapidly moving from the research domain to clinical practice. The use of AI in cardiac imaging provides numerous benefits, such as improving efficiency, reducing human error, and increasing diagnostic accuracy. Techniques such as echocardiography, coronary computed tomography angiography (CCTA), and cardiac magnetic resonance imaging (CMR) can all benefit significantly from AI-based solutions, improving the reproducibility of quantitative morphological and functional information and ultimately improving patient outcomes. Convolutional neural network-based solutions are aiding the field of imaging acquisition and reconstruction by significantly reducing radiation and contrast dose for CCTA imaging while improving image quality. In cardiac CT imaging, numerous algorithms are under development or already available for clinical use for the fast and accurate evaluation of the coronary tree. These include AI-powered software for the interpretation of stenosis and plaque analysis and automated CAC scoring with excellent agreement with human readers. Computational fluid dynamics computed tomography fractional flow reserve (CT-FFRCFD) is the only FDA-approved clinical solution for CT-FFR assessment; however, machine learning-based CT-FFR (CT-FFRML) has been developed for rapid in-house CT-FFR assessment for research application, demonstrating similar accuracy to CFD-based CT-FFR. In the future, AI algorithms for CCTA will likely incorporate identifying vulnerable plaque features and quantifying plaque burden to improve cardiovascular disease risk assessment. AI algorithms for CMR can automate LV function quantification and reduce variability in readings, making CMR a more efficient and reproducible option for assessing cardiac function, valvular disease, perfusion, and blood flow. Integrating data from various imaging modalities with AI-based solutions is the ultimate goal for seamless clinical workflow.