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Stanford Algorithm Can Diagnose Pneumonia Better Than Radiologists

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Radiologist Matthew Lungren considers the results of detections made by the algorithm.

A new deep-learning algorithm developed by Stanford University researchers evaluates chest x-rays for signs of disease.

Credit: L.A. Cicero

Researchers at Stanford University say they have developed a deep-learning algorithm that evaluates chest x-rays for signs of disease and can outperform radiologists at diagnosing pneumonia.

The researchers say the algorithm also can diagnose up to 13 other types of medical conditions with greater accuracy than previous state-of-the-art methods.

The algorithm uses a public dataset, initially released by the U.S. National Institutes of Health Clinical Center, containing 112,120 frontal-view chest x-rays with up to 14 possible pathologies.

The researchers had a group of radiologists independently annotate 420 images for possible indications of pneumonia, and they developed a computer-based tool that produces a heat map of the chest x-rays, but instead of representing temperature the map colors represent areas most likely indicating pneumonia.

"We plan to continue building and improving upon medical algorithms that can automatically detect abnormalities and we hope to make high-quality, anonymized medical datasets publicly available for others to work on similar problems," says Stanford graduate student Jeremy Irvin.

From Stanford News
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Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA


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