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image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated.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

The artificial intelligence-driven Caption Guidance software guides point of care ultrasound (POCUS) users to get optimal cardiac ultrasound images. The AI software is an example of a FDA-cleared software that is helping improve imaging, even when used by less experienced users.

The artificial intelligence-driven Caption Guidance software guides point of care ultrasound (POCUS) users to get optimal cardiac ultrasound images. The AI software is an example of a FDA-cleared software that is helping improve imaging, even when used by less experienced users.

Feature | Artificial Intelligence | September 29, 2020 | Joe Fornadel, J.D., and Wes Moran, J.D.
The number of Federal Drug Administration (FDA)-approved AI-based algorithms is significant and has grown at a steady
Selfied might be used with AI to identify patients with heart disease. Getty Images

Getty Images

News | Artificial Intelligence | August 24, 2020
August 24, 2020 — Sending a photo selfie to the doctor could be a cheap and simple way of detecting heart disease usi
aption Health a leader in medical AI technology, has received U.S. Food and Drug Administration (FDA) 510(k) clearance for an updated version of Caption Interpretation, which uses artificial intelligence (AI) to enable clinicians to obtain quick, easy and accurate measurements of cardiac ejection fraction (EF) at the point of care.

Caption Health a leader in medical AI technology, has received U.S. Food and Drug Administration (FDA) 510(k) clearance for an updated version of Caption Interpretation, which uses artificial intelligence (AI) to enable clinicians to obtain quick, easy and accurate measurements of cardiac ejection fraction (EF) at the point of care.

News | Artificial Intelligence | August 19, 2020
August 19, 2020 — Caption Health a leader in medical AI technology, has received U.S.
In February 2020, the U.S. Food and Drug Administration (FDA) cleared artificial intelligence software to assist in the acquisition of cardiac ultrasound images. The Caption Guidance software from Caption Health is an accessory to compatible diagnostic ultrasound systems and uses artificial intelligence to help the user capture images of a patient’s heart that are of acceptable diagnostic quality. It is aimed at point of care ultrasound (POCUS) exams, where users may not be regular sonographers.

In February 2020, the U.S. Food and Drug Administration (FDA) cleared artificial intelligence software to assist in the acquisition of cardiac ultrasound images. The Caption Guidance software from Caption Health is an accessory to compatible diagnostic ultrasound systems and uses artificial intelligence to help the user capture images of a patient’s heart that are of acceptable diagnostic quality. It is aimed at point of care ultrasound (POCUS) exams, where users may not be regular sonographers.

Feature | Artificial Intelligence | August 18, 2020 | Dave Fornell, Editor
The No.
An example of DiA'a automated ejection fraction AI software on the GE vScan POCUS system at RSNA 2019.

An example of DiA'a automated ejection fraction AI software on the GE vScan POCUS system at RSNA 2019.

News | Artificial Intelligence | May 26, 2020
May 26, 2020 — DiA Imaging Analysis, a provider of AI based ultrasound analysis solutions, said it received a governm
A list of all the abnormalities the AI model classifies. This illustration only shows three representative leads (DII, V1 and V6).Fig. This is Figure 1 from the Nature Communications article.

A list of all the abnormalities the AI model classifies. This illustration only shows three representative leads (DII, V1 and V6).Fig. This is Figure 1 from the Nature Communications article.

News | Artificial Intelligence | May 19, 2020
May 19, 2020 — Artificial inte...