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

Download Mobile App




Deep Learning Model Accurately Classifies Chest X-Rays

By HospiMedica International staff writers
Posted on 16 Dec 2019
Print article
Image: Chest X-ray of a pneumothorax missed by radiologist (L), but identified by the DL model (R) (Photo courtesy of Google Health)
Image: Chest X-ray of a pneumothorax missed by radiologist (L), but identified by the DL model (R) (Photo courtesy of Google Health)
Combining deep learning (DL) models with adjudicated image labels can help classify clinically important findings on chest X-rays, claims a new study.

Researchers at Google Health (Mountain View, CA), Apollo Radiology International (Hyderabad, India), California Advanced Imaging (Novato, USA), and other institutions have developed DL models that can accurately classify four clinically important chest X-ray findings - pneumothorax, nodules and masses, fractures, and airspace opacities. The target findings were selected in consultation with radiologists and clinical colleagues, so as to focus on conditions that are both critical for patient care, and for which chest X-ray images alone are an important and accessible first-line imaging study.

To do so, they used two large data sets. The first included 759,611 images from the Apollo Hospitals network (Hyderabad, India), and the second was drawn from a publicly available set of 112,120 images. Natural language processing and expert review of a small subset of images were then used to provide labels for 657,954 training images, with reference standards defined by four radiologists. The results showed that for all four radiologic findings, and across both datasets, DL models exhibited radiologist-level performance. The study was published on December 3, 2019, in Radiology.

“Achieving expert-level accuracy on average is just a part of the story. Even though overall accuracy for the DL models was consistently similar to that of radiologists for any given finding, performance for both varied across datasets,” said senior author Shravya Shetty, MSc, technical lead of Google Health. “This highlights the importance of validating deep learning tools on multiple, diverse datasets, and eventually across the patient populations and clinical settings in which any model is intended to be used.”

With millions of diagnostic examinations performed annually worldwide, chest X-rays are an important and accessible clinical imaging tool for the detection of many diseases. However, their usefulness can be limited by challenges in interpretation, which requires rapid, thorough evaluation of a two-dimensional image depicting complex, three-dimensional (3D) organs and disease processes. As a result, early-stage lung cancers or pneumothoraces (collapsed lungs) can often be missed, potentially leading to serious adverse outcomes.

Related Links:
Google Health
Apollo Radiology International
California Advanced Imaging


Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
Color Doppler Ultrasound System
KC20

Print article

Channels

Critical Care

view channel
Image: The permeable wearable electronics developed for long-term biosignal monitoring (Photo courtesy of CityUHK)

Super Permeable Wearable Electronics Enable Long-Term Biosignal Monitoring

Wearable electronics have become integral to enhancing health and fitness by offering continuous tracking of physiological signals over extended periods. This monitoring is crucial for understanding an... Read more

Surgical Techniques

view channel
Image: NTT and Olympus have begun the world\'s first joint demonstration experiment of a cloud endoscopy system (Photo courtesy of Olympus)

Cloud Endoscopy System Enables Real-Time Image Processing on the Cloud

Endoscopes, which are flexible tubes inserted into the body's natural openings for internal examination and biopsy collection, are becoming increasingly vital in medical diagnostics. Their minimal invasiveness... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The PATHFAST hs-cTnI-II high-sensitivity troponin assay has been developed for the PATHFAST Biomarker Analyzer (Photo courtesy of Polymedco)

POC Myocardial Infarction Test Delivers Results in 17 Minutes

Chest pain is the second leading cause of emergency department (ED) visits by adults in the United States, generating over 7 million visits annually. In the event of a suspected heart attack, physicians... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.