New research finds that rapid MRI scans generated with artificial intelligence (AI) were just as effective as, and were diagnostically interchangeable with, traditional MRI. The results could significantly improve the patient experience, expand access to MRIs, and potentially enable new use cases for MRI.
For the study, researchers at NYU Grossman School of Medicine and Facebook AI built a neural network and trained it using the world’s largest open-source dataset of deidentified knee MRIs, which was created and shared by NYU Langone Health as part of the fastMRI initiative it launched with Facebook two years ago.
By removing roughly three-fourths of the raw data used to create a scan, the AI model was able to generate a fastMRI scan that matched the scan created by the standard slower MRI process. Because the fastMRI scans require four times less data, patients can be imaged much faster and spend less time in the scanning machine.
Musculoskeletal radiologists reviewed two sets of knee MRIs from 108 patients, one set using the standard imaging techniques and one set using the fastMRI AI model. The results, published in the American Journal of Roentgenology, found no significant differences in the radiologists’ evaluations. The radiologists found the same abnormalities and arrived at the same diagnoses regardless of whether they were examining the standard or the AI-generated MRIs. In addition, all the radiologists judged the AI-accelerated images to be of better overall quality than the traditional ones.
“This study is an important step toward clinical acceptance and utilization of AI-accelerated MRI scans because it demonstrates for the first time that AI-generated images are essentially indistinguishable in appearance from standard clinical MRI exams and are interchangeable in regards to diagnostic accuracy,” says Michael P. Recht, MD, chair and the Louis Marx Professor of Radiology in the Department of Radiology at NYU Langone and lead author of the study. “This marks an exciting paradigm shift in how we are able to improve the patient experience and create images.