Imaging Informatics

RBJ asked for—and received—in-depth answers to six high-level questions about data analytics. What all these Q&A sets have in common is the supplying of a fresh insight or two (or three) into tapping data for its power to prove value and bolster the bottom line.

Speech recognition has become a staple software category in radiology over the past three decades, and other medical specialties have adopted it as well. Yet efforts to assess the toolset’s applications and adaptations have been frustrated by the lack of a unified set of metrics.

Middle-aged smokers have smaller gray-matter volumes than their non-smoking peers, and the falloff is especially pronounced in the brains of smokers who also drink alcohol.

The Global Diagnostic Imaging Healthcare IT & Radiation Therapy Trade Association (DITTA) has published a new white paper on cybersecurity, providing medical technology manufacturers with a list of recommended best practices.

Flywheel, a Minneapolis, Minnesota-based research informatics company, has announced a new partnership with Google Cloud to provide researchers an increased selection of solutions through its platform.

Electronic medical records (EMRs) contain mounds of valuable, but unformatted information making it difficult to use as a source for research, wrote first author, Changhwan Lee, with Hanyang University in Seoul, Korea, and colleagues.  AI may be able to solve that problem.

If you’ve seen one data center, you’ve seen them all. That’s what Charles Rivers believed, at least.

Like every American academic healthcare institution, SUNY Downstate Medical Center in Brooklyn, N.Y., is a beehive of activity in three overlapping yet distinct areas of focus—patient care, physician education and medical research. 

Bill Lacy, vice president of medical informatics at FUJIFILM Medical Systems U.S.A., spoke with Radiology Business about AI’s impact on radiologist workflow and what the company has planned for HIMSS19.

Researchers have demonstrated the use of natural language processing (NLP) to identify urinary-tract stones in positive radiology reports on CT scans of the kidneys, ureter and bladder. 

“Integration of a scoring system into structured prostate MRI reports could be of great value to clinical research as well as routine clinical care...," wrote authors of a new study published in the American Journal of Roentgenology, which examined how background signal-intensity changes affect prostate cancer detection.

A recurrent neural network (RNN) can be trained to automatically classify important findings in unstructured radiology reports, according to new research published in the American Journal of Roentgenology