News | Magnetic Resonance Imaging (MRI) | September 25, 2019

Machine Learning Could Offer Faster, More Precise Cardiac MRI Scan Results

U.K. study finds cardiac MRI scans can be read by artificial intelligence 186 times faster than humans, with comparable precision to experts

Machine Learning Could Offer Faster, More Precise Cardiac MRI Scan Results

September 25, 2019 – Cardiac magnetic resonance imaging (MRI) analysis can be performed significantly faster with similar precision to experts when using automated machine learning, according to new research. The study was published in Circulation: Cardiovascular Imaging, an American Heart Association journal.[1]

Currently, analyzing heart function on cardiac MRI scans takes approximately 13 minutes for humans. Utilizing artificial intelligence (AI) in the form of machine learning, a scan can be analyzed with comparable precision in approximately four seconds.

Healthcare professionals regularly use cardiac MRI scans to make measurements of heart structure and function that guide patient care and treatment recommendations. Many important clinical decisions including timing of cardiac surgery, implantation of defibrillators, and continuing or stopping cardiotoxic chemotherapy, rely on accurate and precise measurements. Improving the performance of these measures could potentially improve patient management and outcomes.

In the U.K., where the study was conducted, it is estimated that more than 150,000 cardiac MRI scans are performed each year. Based on the number of scans per year, researchers believe that utilizing AI to read scans could potentially lead to saving 54 clinician-days per year at each U.K. health center.

Researchers trained a neural network to read the cardiac MRI scans and the results of almost 600 patients. When the AI was tested for precision compared to an expert and trainee on 110 separate patients from multiple centers, researchers found that there was no significant difference in accuracy.

“Cardiovascular MRI offers unparalleled image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated. Automated machine learning techniques offer the potential to change this and radically improve efficiency, and we look forward to further research that could validate its superiority to human analysis,” said study author Charlotte Manisty, M.D. Ph.D. “Our dataset of patients with a range of heart diseases who received scans enabled us to demonstrate that the greatest sources of measurement error arise from human factors. This indicates that automated techniques are at least as good as humans, with the potential soon to be ‘super-human’ — transforming clinical and research measurement precision.”

Although the study did not demonstrate superiority of AI over human experts and was not used prospectively for clinical assessment of patient outcomes, this study highlights the potential that such techniques could have in the future to improve analysis and influence clinical decision making for patients with heart disease.

For more information: www.ahajournals.org/journal/circimaging

 

Reference

1. Bhuva A.N., Bai W., Lau C., et al. A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis. Circulation: Cardiovascular Imaging, published online Sept. 24, 2019. https://doi.org/10.1161/CIRCIMAGING.119.009214

Related Content

 MaxQ AI
News | Artificial Intelligence | November 13, 2019
November 13, 2019 – MaxQ AI announced a new partnership agreement with...
An image on Brigham and Women's Hospital's 7T MRI system

An image on Brigham and Women's Hospital's 7T MRI system. Image courtesy of Brigham and Women's Hospital

News | Magnetic Resonance Imaging (MRI) | November 13, 2019
November 13, 2019 — Increased immune system activity along the surface of the brain, or meningeal inflammation, may b
 Paxera Ultima 360
News | Enterprise Imaging | November 12, 2019
November 12, 2019 — Medical Imaging developer PaxeraHealth will showcase the
 Lunit RSNA
News | Artificial Intelligence | November 12, 2019
November 12, 2019 — Lunit, a leading medical AI software company devoted to provi
Radiographer Apollo Exconde with his Lego concept open MRI for patient education.

Radiographer Apollo Exconde with his Lego concept open MRI for patient education.

News | Patient Engagement | November 11, 2019
November 11, 2019 — Radiographer Apollo Exconde...
 Laurel Bridge Machine Learning workflow
News | Artificial Intelligence | November 08, 2019
November 8, 2019 — Laurel Bridge Software announces the new Laurel Bridge
Image by Dr. Manuel González Reyes from Pixabay

Image by Dr. Manuel González Reyes from Pixabay 

News | SPECT Imaging | November 08, 2019
November 8, 2019 — Using ground-breaking technology, researchers at the...
This chest X-ray of a patient being treated for e-cigarette or vaping-associated lung injury shows lung opacities, densities and whitish cloud-like areas which are typically seen with unusual pneumonias, fluid in lungs or lung inflammation. Image courtesy of Intermountain Healthcare

This chest X-ray of a patient being treated for e-cigarette or vaping-associated lung injury shows lung opacities, densities and whitish cloud-like areas which are typically seen with unusual pneumonias, fluid in lungs or lung inflammation. Image courtesy of Intermountain Healthcare

News | Clinical Trials | November 08, 2019
November 8, 2019 — As the outbreak of lung injuries and deaths associated with e-cigarettes, or...
Unlike other technologies for imaging the placenta, pCASL MRI can distinguish maternal blood from fetal blood

Image courtesy of Pixabay

News | Clinical Trials | November 07, 2019
November 7, 2019 — A new imaging technique to track