Medical Imaging

NinePoint Medical, a Massachusetts-based medical device company, has received market clearance from the FDA for its new AI-based platform for image feature segmentation.

Researchers are hopeful a newly-developed machine-learning algorithm can be used to improve the detection of benign polyps during colonoscopies following a recent study validating the method.

A deep learning technique was able to detect glaucoma with more accuracy than traditional approaches, according to a recent study conducted by IBM and New York University scientists.

A deep-learning (DL) algorithm was able to assess mammographic breast density at the level of an experienced mammographer, according to a study published in Radiology.

PACS is powering better workflow in breast imaging, transforming the way breast imaging radiologists read studies and interact with one another by improving physician efficiency, accuracy and saving time. Metrics matter in healthcare today and now excellent efficiency, productivity, quality of care and provider and patient satisfaction are measures of success that belong together in the pursuit of better breast imaging.

Having been in the Sectra PACS fold since 2004, members of the radiology department at six-hospital CoxHealth in Springfield, Mo., didn’t need much convincing to “VNAble” their existing system so it could handle cardiology workflows on top of their own.

The global market for artificial intelligence (AI) in medical imaging is expected to see significant growth in the years ahead, topping $2 billion by 2023, according to a new report from Signify Research.

Building the infrastructure to support the accelerating adoption of AI in healthcare is the mission of Pure Storage and its FlashBlade technology, an all-flash scale-out object-based solution that can expand to petabytes of capacity. As Esteban Rubens says, infrastructure to power AI, machine learning and deep learning needs to be effortless, efficient and evergreen to ensure success today and into the future. Here’s how.

When it comes to teaching new dogs new tricks, radiology training programs need to be thinking about updating their curricula and preparing for both the short- and the long-term effects of AI and machine learning, according to “Toward Augmented Radiologists,” a new commentary published online in March in Academic Radiology.

Ever the visionary, Paul Chang sees AI as an asset to radiologists. As he sees it, “AI and deep learning doesn’t replace us. It frees us to do more valuable work.” The vice chair of radiology informatics at University of Chicago Medicine takes a quick look through the crystal ball at the four stand-out challenges facing radiology with the rise of AI.

To look into the future is to catch only a glimpse inside Simon Warfield’s radiology research lab at Boston Children’s Hospital. His team is pairing hyperfast imaging and deep learning to push the limits of medical imaging and artificial intelligence (AI) to identify, prevent and treat disease. He’s also eyeing ways AI will help as data sharing expands among research sites. “The research world needs to look forward to manage forward,” he says.

Lawrence Tanenbaum is a big believer in AI, as a tool to create better images, offer a more comprehensive view of a patient and more effectively handle imaging’s increasing volume and complexity. Bigger yet, AI is the impetus to change the way radiology and medicine are practiced across the care spectrum.