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image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated.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

While electronic medical record systems have helped consolidate most patient data into one location, medical imaging IT systems has proved to be more difficult to replicate by large EMR vendors. This has made room in the market for third-party radiology IT vendors that allow easy integration with the larger EMRs like Epic and Cerner. This image shows Agfa's enterprise imaging system, leveraging its ability to be accessed anywhere with internet connection and pull images from radiology and surgery.

While electronic medical record systems have helped consolidate most patient data into one location, medical imaging IT systems has proved to be more difficult to replicate by large EMR vendors. This has made room in the market for third-party radiology information system vendors that allow easy integration with the larger EMRs like Epic and Cerner. This image shows Agfa's enterprise imaging system, leveraging its ability to be accessed anywhere with an internet connection and able to pull in images from both radiology and surgery. 

Feature | Enterprise Imaging | October 17, 2019 | Steve Holloway
October 17, 2019 — The growing influence and uptake of electronic medical records (EMRs) in healthcare has driven deb
USF Health Expands Digisonics System With Vascular Reporting
News | Cardiac PACS | October 17, 2019
University of South Florida (USF) Health in Tampa, Fla., has enhanced their use of the Digisonics Cardiovascular...
Guerbet Signs Agreement With Icometrix for Exclusive Distribution of Icobrain
News | Neuro Imaging | October 16, 2019
Guerbet announced it has signed an exclusive agreement with Icometrix for the distribution in France, Italy and Brazil...
Subtle Medical Receives FDA 510(k) Clearance for AI-powered SubtleMR
Technology | Artificial Intelligence | October 16, 2019
Subtle Medical announced 510(k) clearance from the U.S. Food and Drug Administration (FDA) to market SubtleMR. SubtleMR...
Using Compressed SENSE for faster MRI scans, healthcare providers can transform their radiology workflow.

Using Compressed SENSE for faster MRI scans, healthcare providers can transform their radiology workflow.

Sponsored Content | Case Study | Magnetic Resonance Imaging (MRI) | October 16, 2019
Since the introduction of magnetic resonan...
Feature | Artificial Intelligence | October 16, 2019 | By Siddharth (Sid) Shah
The period between November through February is pretty interesting for the field of medical imaging — two major confe
Canon Medical Receives FDA Clearance for Vantage Orian 1.5T MRI

Canon Medical Receives FDA Clearance for Vantage Orian 1.5T MRI

Feature | Magnetic Resonance Imaging (MRI) | October 09, 2019 | By Jeff Zagoudis
As the role of artificial intelligence continues to expand, many companies are making significant investments in this technology to offer solutions
Feature | Artificial Intelligence | October 09, 2019 | By Sharmistha Sarkar
Artificial intelligence (AI) is a technology
Sponsored Content | Whitepapers | Clinical Trials | October 09, 2019
A 2019 N G PX REPORT
Patient Treatments With ViewRay's MRIdian Linac Begin in New England
News | Image Guided Radiation Therapy (IGRT) | October 08, 2019
ViewRay Inc. announced today that patient treatments are scheduled to begin in Boston with ViewRay's MRIdian Linac...