Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

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NIH-backed study identifies brain biomarkers tied to severe PTSD

Using fMRI, a team of researchers discovered combat veterans with severe post-traumatic stress disorder (PTSD) demonstrate distinct patterns in how their brain and body respond to learning danger and safety. The study may help explain why some experience more severe symptoms than others.

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Novel 3D imaging tool captures blood flow in the capillaries

Researchers at Northwestern University in Evanston, Illinois have developed a noninvasive, three-dimensional (3D) imaging tool able to capture blood flow and oxygenation within the capillaries of a human, according to research published in the journal Light: Science & Applications. The technique could help detect conditions from headaches to cardiovascular disease, sooner.

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Virginia Tech researchers use brain imaging, AI to diagnose mental illness

In an effort to destigmatize mental illness and help patients find better treatments, researchers from Virginia Tech’s Fralin Biomedical Research Institute in Roanoke, Virginia trained a machine learning algorithm with brain fMRI scans to diagnosis mental disorders more accurately than standard methods, according to a recent report by The Verge.

Radiologists unwilling to understand AI are hindering future students

“Students rely on us to understand how radiology is incorporating new technology and what the future of the field will look like for them, but many of us are ill prepared to teach the younger generation about this, mostly because we ourselves are not sure,” Allison Grayev, MD, wrote in an editorial published in Academic Radiology. 

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Combined deep learning method improves stroke care

A combined deep learning method better detected hemorrhages and identified different subtypes of intracranial hemorrhage than single algorithms used alone, according to a new study published in the Journal of Digital Imaging.

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New imaging technique could extend perception of microscopes, endoscopes

Researchers at Boston University in Massachusetts have developed an imaging technique that, by using a photograph captured with a digital camera, can reconstruct the position of an opaque object and its surroundings when both are out of direct sight, according to a recent report by Nature

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Total-body PET/CT scanner granted FDA approval

The technology can capture three-dimensional (3D) images of the entire human body at one bed position and requires 40-times less radiation than current methods, according to a recent press release.

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4D MRI reliable for diagnosing parathyroid tumors

Four-dimensional (4D) MRI with dynamic contrast-enhanced (DCE) sequencing is a reliable method for localizing parathyroid lesions, reported authors of a single-center study published in the European Journal of Radiology.

Around the web

Harvard’s David A. Rosman, MD, MBA, explains how moving imaging outside of hospitals could save billions of dollars for U.S. healthcare.

Back in September, the FDA approved GE HealthCare’s new PET radiotracer, flurpiridaz F-18, for patients with known or suspected CAD. It is seen by many in the industry as a major step forward in patient care. 

After three years of intermittent shortages of nuclear imaging tracer technetium-99m pyrophosphate, there are no signs of the shortage abating.