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Imaging Informatics


An analysis of nearly three million radiologic exams has confirmed prior research showing that physicians’ concentration tends to fall off toward the tail end of on-duty shifts. And yes, the diminishment in radiologists’ accuracy may be increased when they’re working especially long shifts and/or plowing through long worklists.

Free-text radiology reports can be automatically classified by convolutional neural networks (CNNs) powered by deep-learning algorithms with accuracy that’s equal to or better than that achieved by traditional—and more labor-intensive—natural language processing (NLP) methods.

Researchers in the radiology department at the University of California, San Francisco (UCSF)—led by of Sabrina Ronen, PhD, director of the Brain Research Interest Group (RIG) and professor in the department of radiology and biomedical imaging at UCSF—are in the process of developing new, non-invasive imaging biomarker indicators to address multiple types of cancer, according to a recent UCSF press release

Many radiologists use Twitter and LinkedIn for staying up on matters related to their work. A study published online Nov. 12 in the Journal of the American College of Radiology shows they’d do well to tap, for the same purposes, the social-media platform that’s commonly thought of as a purely personal online space.

A group of German researchers has developed a nuclear medicine test that can detect infections in kidney transplant tissue, according to a study published in Journal of Nuclear Medicine


Recent Headlines

Hospital staff prepares for potential failure of electronic imaging services

Imagine the electronic management system for imaging services fail, leaving radiologists with delayed bookings and examinations of images. Would a hospital's administration, medical staff and/or patients be ready for something like this?

Patients undergo PET/CT scans while using video goggles for distraction

Researchers at the Cincinnati Children’s Hospital Medical Center used video goggles to distract children undergoing PET or CT scans to determine whether they created CT and PET artifacts.

RSNA 2016: Masking index shows promise in predicting the probability of masking in a mammogram

This year’s annual RSNA conference featured a session titled, "Hot Topics in Breast Imaging," which outlined a variety of sub-topics including over diagnosis, trends in breast density assessment over time and breast cancer screening.

RSNA 2016: Image sharing says goodbye to compact discs, hello to the cloud

The RSNA 2016 session, "Next Generation Infrastructure for Medical Imaging," introduced participants to the importance of image sharing and exchange with regard to the quality of care a radiologist delivers.

Don’t leave patients out when discussing patient portals

Online portals where patients can schedule appointments with physicians or contact providers are becoming more common, but many don’t include patient medical images or a way to contact radiologists. If practices are planning on building such a system, they shouldn’t leave patients out of the process.

Enterprise Imaging Means Never Having to Say You’re Siloed

Memo to radiology: It’s time to quicken the pace at which you’re metamorphosing out of your role as chief keeper of all things imaging. The era of enterprise imaging is upon U.S. healthcare, and your speedy adaptation will benefit you as well as your patients and referrers.

Q&A: Chris Tomlinson on Building the Network of the Future with Image Exchange

Chris Tomlinson, MBA, CRA, FAHRA, of CHOP and RACH envisions a large pediatric radiology network where everyone is connected and imaging results can easily be shared in seconds.

3 ways machine learning will disrupt radiology—and the rest of medicine with it

Machine learning’s expansive capacity to quickly turn big health data into evidence-based care will challenge all practitioners of medicine to either grow along with the technology or accept getting left behind by it. And radiologists will be among the first to feel its push (if they’re not among the rads who are already working with it). 

4 rads: Take all imaging to the cloud—and let breast specialists lead the way

Four breast radiologists are calling for the creation of a national imaging repository housed in the cloud and spearheaded by their specialty. 

Radiologist bests machine-learning algorithms at diagnosing thyroid cancer

In developing algorithms to differentiate between suspicious nodules in the thyroid gland, researchers in China have found that their machine-learning computations separate malignant from benign properties more accurately than an inexperienced radiologist—but not as accurately as the experienced radiologist whose know-how was used to create the algorithms.