We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Algorithm Outperforms Radiologists in Detecting Pneumonia on X-Rays

By MedImaging International staff writers
Posted on 21 Nov 2017
Print article
Image: The algorithm CheXNet can diagnose up to 14 types of medical conditions, including pneumonia (Photo courtesy of Stanford ML Group).
Image: The algorithm CheXNet can diagnose up to 14 types of medical conditions, including pneumonia (Photo courtesy of Stanford ML Group).
A deep learning algorithm developed by researchers from the Stanford University (Stanford, CA, USA) that evaluates chest X-rays for signs of disease has outperformed expert radiologists at diagnosing pneumonia in just over a month of its development. A paper about the algorithm named CheXNet, which can diagnose up to 14 types of medical conditions, was published November 14 on the open-access, scientific preprint website arXiv.

Soon after the National Institutes of Health Clinical Center recently released a public dataset containing 112,120 frontal-view chest X-ray images labeled with up to 14 possible pathologies, the Machine Learning Group at Stanford began developing an algorithm that could automatically diagnose the pathologies. Meanwhile, four Stanford radiologists independently annotated 420 of the images for possible indications of pneumonia. Within a week the researchers had developed an algorithm that diagnosed 10 of the pathologies labeled in the X-rays more accurately than the previous state-of-the-art results. In just over a month, CheXNet could beat these standards in all 14 identification tasks and also outperformed the four individual Stanford radiologists in pneumonia diagnoses.

The Stanford researchers have also developed a computer-based tool that produces what appears to be a heat map of chest X-rays, although instead of representing temperature, the colors of these maps represent the areas determined by the algorithm as the ones most likely to represent pneumonia. The tool could help reduce the amount of missed pneumonia cases and significantly accelerate the workflow of radiologists by indicating where to look first, resulting in faster diagnoses for the sickest patients.

“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,” said Jeremy Irvin, a graduate student in the Machine Learning group and co-lead author of the paper. “There is massive potential for machine learning to improve the current health care system, and we want to continue to be at the forefront of innovation in the field.”

Related Links:
Stanford University

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Laptop Ultrasound Scanner
PL-3018
Digital Radiography Acquisition Software
VXvue with PureImpact
Dose Calibration Electrometer
PC Electrometer

Print article

Channels

Radiography

view channel
:	Image: The AI model could be a valuable adjunct to human radiologists in breast cancer diagnoses and risk prediction (Photo courtesy of 123RF)

AI Model Predicts 5-Year Breast Cancer Risk from Mammograms

Approximately 13% of U.S. women, or one in every eight, are predicted to develop invasive breast cancer over their lifetime, with 1 in 39 women (3%) succumbing to the illness, according to the American... Read more

Nuclear Medicine

view channel
Image: The AI system uses scintigraphy imaging for early diagnosis of cardiac amyloidosis (Photo courtesy of 123RF)

AI System Automatically and Reliably Detects Cardiac Amyloidosis Using Scintigraphy Imaging

Cardiac amyloidosis, a condition characterized by the buildup of abnormal protein deposits (amyloids) in the heart muscle, severely affects heart function and can lead to heart failure or death without... Read more

General/Advanced Imaging

view channel
Image: The CIARTIC Move self-driving mobile C-arm has received FDA clearance (Photo courtesy of Siemens)

Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery

Intraoperative imaging faces significant challenges due to staff shortages and the high demands placed on surgical teams in the operating room (OR). A common challenge during many OR procedures is the... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.