AI Can Read A Cardiac MRI In 4 Seconds: Do We Still Need Human Input?

An MRI scan read in 4 seconds? Are we hearing this right?

You are, and welcome to the present and the future of automated machine learning programs that have the ability to significantly increase the speed of analysis of specialized MRI scans.

Don’t worry though—it’s not ready for prime time just yet!

Now, new research sheds light on just far we have come in terms of development of such machine learning programs.

According to a new study published in the journal, Circulation: Cardiovascular Imaging, analysis of cardiac MRI scans using automated machine learning can be performed significantly faster and with comparable accuracy to human interpretation by trained cardiologists.

It generally takes about 13 minutes for a trained physician (cardiologist) to interpret a cardiac MRI. But with the advent of artificial intelligence (AI) and machine learning algorithms, a scan can be analyzed with similar accuracy in approximately four seconds–nearly 186 times faster!

Cardiac MRI scans help cardiologists make preoperative decisions for surgical planning by providing important measurements of heart structure and function.

But they may also help to make decisions related to timing of cardiac surgery, implantation of defibrillators and continuing or discontinuing infusions of cardiotoxic chemotherapy for cancer patients. Such clinical decisions ultimately rely on accurate and precise measurements from cardiac MRI scans.  

Acceleration of data gleaned from such MRI scans might also help to improve speed of clinical decision-making and subsequent health outcomes, ultimately affecting care from a population health perspective.

The study was performed in the UK, where approximately 150,000 cardiac MRI scans are performed annually. Extrapolating the number of scans performed per year, the investigators calculated that using AI to interpret scans could potentially result in saving 54 clinician-days annually at each health facility.

For their study, researchers developed and trained a neural network to read the cardiac MRI scans of nearly 600 patients. They found that there was no significant difference in accuracy when the machine learning algorithm was tested for precision compared to an expert and trainee on 110 separate patients from multiple centers.