“To date, most cardiac parameters derived from CMR images are obtained using manual measurements, including chamber volumes that rely on tracing of endocardial boundaries,” wrote Neha Goyal, University of Chicago Medicine, and colleagues. “Automated identification of cardiac chambers followed by accurate measurements without time-consuming and experience-dependent user input would be a major development in clinical cardiac imaging.”
The researchers explored data from 21 patients undergoing clinical CMR examinations for a variety of reasons. While an AI algorithm provided LV time-volume curves from CMR cine images, an experienced reader manually calculated the same measurements. LV volumes and ejection/filling parameters determined by the algorithm and the experienced specialist were then compared.
Overall, the authors found that time-volume curves “were similar between the two techniques in magnitude and shape.” And as one might expect, collecting the necessary data was much more efficient using the AI technique. Generating the time-volume curves took approximately 2.5 minutes per patient using AI, but approximately 43 minutes when done manually.
