AI system accurately interprets cardiac MRI scans

Experts have developed an artificial intelligence-powered system they say can significantly improve cardiac imaging analysis. 

Cardiac MRIs are known to be among some of the most challenging imaging exams to interpret; the process is time-intensive, even for the most seasoned radiologists. As such, simplifying these interpretations has been the target of experts for many years. 

Developed by a team of researchers from Carnegie Mellon University with help from Cleveland Clinic’s Cardiovascular Innovation Research Center, experts are hopeful the system—called “CMR-CLIP”—can support radiologists tasked with interpreting cardiac MRIs. The vision language model combines moving images from the scans with patients’ associated reports. 

“Cardiac MRI interpretation is highly specialized and time intensive,” said co-principal investigator of the analysis David Chen, PhD, with the Cleveland Clinic. “Systems like CMR-CLIP have the potential to support clinicians through automated screening, and interpretation support, particularly in settings where expert readers are limited. Such reader assistant tools are critical to improving patient access to this powerful diagnostic technology.” 

CMR-CLIP (cardiovascular magnetic resonance imaging-contrastive language image pretraining) was trained on over 11,000 cardiac MRI exams and their corresponding reports. This enabled researchers to develop a capable model without the time-consuming task of manually annotating training data. Through this, the model was able to learn how physicians describe these exams in clinical settings.