Artificial Intelligence Innovations in Cardiac Imaging

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Artificial Intelligence Innovations in Cardiac Imaging

December 17, 2019 by 2012pharmaceutical

Artificial Intelligence Innovations in Cardiac Imaging

Reporter: Aviva Lev-Ari, PhD, RN

3.3.23

3.3.23 Artificial Intelligence Innovations in Cardiac Imaging, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

‘CTA-for-All’ fast-tracks intervention, improves LVO detection in stroke patients

Anicka Slachta | November 15, 2019 | Cardiovascular Imaging

A “CTA-for-All” stroke imaging policy improved large vessel occlusion (LVO) detection, fast-tracked intervention and improved outcomes in a recent study of patients with acute ischemic stroke (AIS), researchers reported in Stroke.

“Combined noncontrast computed tomography (NCCT) and CT angiography (CTA) have been championed as the new minimum standard for initial imaging of disabling stroke,” Mayer, a neurologist at Henry Ford Hospital in Detroit, and co-authors wrote in their paper. “Patient selection criteria that impose arbitrary limits on time from last known well (LKW) or baseline National Institutes of Health Stroke Scale (NIHSS) score may delay CTA and the diagnosis of LVO.”

“These findings suggest that a uniform CTA-for-All imaging policy for stroke patients presenting within 24 hours is feasible and safe, improves LVO detection, speeds intervention and can improve outcomes,” the authors wrote. “The benefit appears to primarily affect patients presenting within six hours of symptom onset.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/cta-all-fast-tracks-intervention-improves-lvo-detection-stroke?utm_source=newsletter&utm_medium=cvb_cardio_imaging

How to integrate AI into the cardiac imaging pipeline

Anicka Slachta | December 05, 2019 | Cardiovascular Imaging

Hsiao said physicians can expect “a little bit of generalization” from neural networks, meaning they’ll work okay on data that they’ve never seen, but they’re not going to produce perfect results the first time around. If a model was trained on 3T MRI data, for example, and someone inputs 1.5T MRI data, it might not be able to analyze that information comprehensively. If some 1.5T data were fed into the model’s training algorithm, though, that could change.

According to Hsiao, all of this knowledge means little without clinical validation. He said he and his colleagues are working to integrate algorithms into the clinical environment such that a radiologist could hit a button and AI could auto-prescribe a set of images. Even better, he said, would be the ability to open up a series and have it auto-prescribe itself.

“That’s where we’re moving next, so you don’t have to hit any buttons at all,” he said.

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/how-integrate-ai-cardiac-imaging-pipeline?utm_source=newsletter&utm_medium=cvb_cardio_imaging

DiA Imaging, IBM pair to take the subjectivity out of cardiac image analysis

Anicka Slachta | December 05, 2019 | Cardiovascular Imaging

IBM Watson Health is adding startup DiA Imaging Analysis to its AI Marketplace in an effort to offer clinicians access to more objective and accurate ultrasound analysis, the company announced Dec. 1.

DiA, an IBM Alpha Zone Accelerator Alumni Startup, has developed AI-powered cardiac ultrasound software that’s already been cleared by the FDA. According to a release, the software was designed to help physicians analyze cardiac ultrasound images automatically and more objectively, since image interpretation is inherently a somewhat subjective process.

“Our collaboration with IBM Watson Health demonstrates the implementation of DiA’s vision to make the analysis of ultrasound images smarter and accessible to clinicians with various levels of experience on any platform,” DiA CEO and co-founder Hila Goldman-Aslan said in a statement.

IBM will focus specifically on DiA’s LVivo EF solution, an application with an AI-based quantification solution that provides clinicians with automated clinical data like ejection fraction and global longitudinal strain.

“IBM Watson Health is proud to announce a collaboration with DiA Imaging,” Anne Le Grand, general manager of imaging, life sciences and oncology at IBM, said. “DiA’s innovative AI-powered offerings can provide our clients with the ability to analyze images with advanced AI-based solutions which can support IBM Watson Health’s mission to help build smarter ecosystems.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/dia-imaging-ibm-partner-cardiac-image-analysis?utm_source=newsletter&utm_medium=cvb_cardio_imaging

FDA clears Ultromics’ AI-based CV image analysis system

Anicka Slachta | November 18, 2019 | Cardiovascular Imaging

U.K.-based health tech firm Ultromics has secured 510(K) FDA clearance for its EchoGo Core image analysis system, the company announced Nov. 14.

EchoGo leverages artificial intelligence to calculate left ventricular ejection fraction, LV volumes and automated cardiac strain on ultrasound-based heart scans. The idea, founder and CEO Ross Upton said, is to automate the analysis and quantification of echos so cardiologists can make more informed decisions about care delivery.

“This is an incredibly exciting step toward the future of healthcare,” Upton, a Forbes “30 Under 30” honoree this year, said in a statement, calling the 510(K) clearance “truly a watershed moment” for his company.

Notably, the FDA’s choice to clear Ultromics’ technology means it will be available to a wider population of patients and providers. Based in the U.K., the company has only been independent of the University of Oxford for two years.

Upton said the EchoGo system will make Ultromics the first tech company to use AI for automated strain analysis, which is applicable to some 60 million scans per year and will be reimbursable in the U.S. starting in January. He said EchoGo could be a useful tool for physicians of all experience levels looking to learn more about strain calculations and improve their interpretation of echocardiograms.

The company is already looking ahead to next year, when Upton and his team plan to launch the EchoGo Pro—something they’re promising will be “the first AI system able to predict cardiac disease from echocardiography.”

“We are also planning to expand into other geographic regions, including Europe and Asia,” Upton said. “Our goal is to improve patient outcomes through earlier detection of cardiac disease.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/fda-clears-ai-based-cv-image-analysis-system?utm_source=newsletter&utm_medium=cvb_cardio_imaging

Smartphone app accurately finds, identifies CV implants—and fast

Anicka Slachta | October 07, 2019 | Cardiovascular Imaging

According to the study, the finalized model achieved 95% sensitivity and 98% specificity.

Ferrick et al. said that since their training sample size was somewhat small and limited to a single institution, it would be valuable to validate the model externally. Still, their neural network was able to accurately identify CIEDs on chest radiographs and translate that ability into a phone app.

“Rather than the conventional ‘bench-to-bedside’ approach of translational research, we demonstrated the feasibility of ‘big data-to-bedside’ endeavors,” the team said. “This research has the potential to facilitate device identification in urgent scenarios in medical settings with limited resources.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/smartphone-app-accurately-finds-identifies-cv-implants?utm_source=newsletter&utm_medium=cvb_cardio_imaging

Machine learning cuts cardiac MRI analysis from minutes to seconds

Anicka Slachta | September 24, 2019 | Cardiovascular Imaging

“Cardiovascular MRI offers unparalleled image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated,” Manisty said in a statement. “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.”

It’s estimated that around 150,000 cardiac MRIs are performed in the U.K. each year, she said, and based on that number, her team thinks using AI to read scans could mean saving 54 clinician-days per year at every health center in the country.

“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,” Manisty said. “This indicates that automated techniques are at least as good as humans, with the potential soon to be ‘superhuman’—transforming clinical and research measurement precision.”

SOURCE

https://www.cardiovascularbusiness.com/topics/cardiovascular-imaging/machine-learning-speeds-cardiac-mri-analysis?utm_source=newsletter&utm_medium=cvb_cardio_imaging

General SOURCE

From: Cardiovascular Business <news@mail.cardiovascularbusiness.com>

Reply-To: Cardiovascular Business <news@mail.cardiovascularbusiness.com>

Date: Tuesday, December 17, 2019 at 9:31 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Cardiovascular Imaging | December 2019