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Editor's e-Note
Although AI is still finding its footing in radiology, new uses emerge on a seemingly daily basis. This month, we’re sharing some exciting news from RSNA on how AI can help radiologists read chest X-rays more efficiently and improve diagnosis.

Where do you think the next big AI breakthrough in radiology will be? Let us know what you think on Twitter and/or Facebook.

— Dave Yeager, editor
e-News Exclusive
AI Improves Chest X-Ray Interpretation

AI can detect clinically meaningful chest X-ray findings as effectively as experienced radiologists, according to a study recently published in the journal Radiology. Researchers say their findings, based on deep learning, could provide a valuable resource for the future development of AI chest radiography models.

Chest radiography, one of the most common imaging exams worldwide, is performed to help diagnose the source of symptoms such as cough, fever, and pain. Despite its popularity, the exam has limitations.

“We’ve found that there is a lot of subjectivity in chest X-ray interpretation,” says study coauthor Shravya Shetty, an engineering lead at Google Health in Palo Alto, California. “Significant interreader variability and suboptimal sensitivity for the detection of important clinical findings can limit its effectiveness.”

Deep learning has the potential to improve chest X-ray interpretation, but it has limitations. For example, results derived from one group of patients cannot always be generalized to the population at large.

Researchers at Google Health developed deep learning models for chest X-ray interpretation that overcome some of these limitations. They used two large data sets to develop, train, and test the models. The first data set consisted of more than 750,000 images from five hospitals in India, while the second set included 112,120 images made publicly available by the National Institutes of Health (NIH).

Full story »
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Worth Repeating
“Imagine you can just take a photo and out comes a 3D model, which means that you can now look at that scene that you have taken a picture of [from] all sorts of different viewpoints. You can go inside it, potentially, view it from different angles—you can take old photographs in your photo collection and turn them into a 3D scene and inspect them like you were there, basically.”

Sanja Fidler, PhD, of Nvidia Research, regarding a new AI system that takes 3D rendering to new heights, extrapolating robust detail (shape, 3D geometry, color, and texture) from 2D images
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