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Editor's e-Note
As we have profiled in our latest editions of Radiology Today, deep learning is making awe-inspiring gains in radiology.

At Stanford University in northern California, researchers have upped the ante another notch, creating an algorithm that can harvest the latest data sets released by the National Institutes of Health and finding that their technology can augment, and even surpass, the diagnostic capabilities of radiologists working alone.

These findings are sure to inspire continued debate over the potential of machine learning to assume an increased role in radiological workflow. This month’s E-Newsletter visits both sides of the topic, as we will hear from others who are crunching the numbers with some trepidation.

— Dave Yeager, editor
e-News Exclusive
New Algorithm Can Augment, Outperform
Radiologists’ Diagnostic Abilities

By Taylor Kubota

Treatments for common but devastating diseases that occur in the chest, such as pneumonia, often rely heavily on how doctors interpret radiological imaging. But even the best radiologists are prone to misdiagnoses due to challenges in distinguishing between diseases based on X-rays.

Researchers at Stanford University in California have developed CheXNet, an algorithm that offers diagnoses based off chest X-ray images. CheXNet can diagnose up to 14 types of medical conditions and is reportedly able to diagnose pneumonia better than expert radiologists working alone. A paper about the algorithm was recently published on the open-access, scientific preprint website arXiv.

“Interpreting X-ray images to diagnose pathologies such as pneumonia is very challenging, and we know that there’s a lot of variability in the diagnoses radiologists arrive at,” says Pranav Rajpurkar, a PhD student in the Stanford Machine Learning Group and colead author of the paper. “We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses.”

The work uses a public data set recently released by the National Institutes of Health Clinical Center. That data set contains 112,120 frontal-view chest X-ray images labeled with up to 14 possible pathologies. It was released in tandem with an algorithm that could diagnose many of those 14 pathologies with some success, designed to encourage others to advance that work.

Full story »
In This e-Newsletter
Other Imaging News
Proprietary Technology Analyzes
Ancient Tomb in Greece

High-resolution X-ray imaging equipment is assisting the international research team that, as reported by the University of Cincinnati, discovered and excavated the 3,500-year-old Griffin Warrior Tomb in Pylos, Greece. A proprietary XTEND Portable X-ray unit is being used to image some of these once-in-a-lifetime findings, described by team leader Sharon Stocker, PhD, as being among “the most magnificent displays of prehistoric wealth discovered in mainland Greece in the past 65 years.”

How AI Can Affect Job Prospects
for Radiologists

The Economist takes a candid look at the proliferation of artificial intelligence, urging caution and a long-term view of the labor market.

Machine Learning Invites Medico-Legal Ramifications
As reported in Data Science, radiologists embracing artificial intelligence must be mindful of potential legal wrinkles on the horizon, not least of which are negligence and malpractice.

Video Games Can Promote Brain Health
in Seniors

Playing 3D-platform video games regularly may improve cognitive function in seniors and increase grey matter in the hippocampus, according to the Université de Montréal.
Recently Online
Radiology at the Movies
Cinematic 3D rendering is offering radiologists, students, and patients a vivid, high-tech, front-row view of internal human anatomy. Read more »

Reality Check
Mixed reality is making inroads in radiology. Read more »

Digging for Gold
A new player in the industry, radiomics, is putting advanced imaging data in the hands of radiologists and other medical decision makers. Read more »

A Path to Follow?
Cutting-edge efforts aimed at streamlining the synthesis of radiology and pathology are gaining ground. Read more »
Worth Repeating
“Our findings suggest that we need to help clinicians better understand the impact personal experiences with friends and family members, as well as their patients, have on their practices.”

Craig Evan Pollack, MD, MHS, of the Johns Hopkins University School of Medicine, on a study finding a correlation between a clinician’s breast cancer screening recommendations and his or her social interactions
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