Artificial Intelligence Innovations in Cardiac Imaging
Reporter: Aviva Lev-Ari, PhD, RN
‘CTA-for-All’ fast-tracks intervention, improves LVO detection in stroke patients
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.”
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How to integrate AI into the cardiac imaging pipeline
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.
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DiA Imaging, IBM pair to take the subjectivity out of cardiac image analysis
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FDA clears Ultromics’ AI-based CV image analysis system
Smartphone app accurately finds, identifies CV implants—and fast
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.”
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Machine learning cuts cardiac MRI analysis from minutes to seconds
“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.”
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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
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