News | Artificial Intelligence | October 10, 2017

NIH Clinical Center Releases 100,000-Plus Chest X-ray Datasets to Scientific Community

Compiled from scans of more than 30,000 patients, datasets are intended to help train artificial intelligence algorithms to aid radiologists in diagnosis

NIH Clinical Center Releases 100,000-Plus Chest X-ray Datasets to Scientific Community

October 10, 2017 — The National Institutes of Health (NIH) Clinical Center recently released over 100,000 anonymized chest X-ray images and their corresponding data to the scientific community. The release will allow researchers across the country and around the world to freely access the datasets and increase their ability to teach computers how to detect and diagnose disease. Ultimately, this artificial intelligence mechanism can lead to clinicians making better diagnostic decisions for patients. 

NIH compiled the dataset of scans from more than 30,000 patients, including many with advanced lung disease. Patients at the NIH Clinical Center, the nation’s largest hospital devoted entirely to clinical research, are partners in research and voluntarily enroll to participate in clinical trials. With patient privacy being paramount, the dataset was rigorously screened to remove all personally identifiable information before release.

Reading and diagnosing chest X-ray images may be a relatively simple task for radiologists but, in fact, it is a complex reasoning problem that often requires careful observation and knowledge of anatomical principles, physiology and pathology. Such factors increase the difficulty of developing a consistent and automated technique for reading chest X-ray images while simultaneously considering all common thoracic diseases.

By using this free dataset, the hope is that academic and research institutions across the country will be able to teach a computer to read and process extremely large amounts of scans, to confirm the results radiologists have found and potentially identify other findings that may have been overlooked.

In addition, this advanced computer technology may also be able to:

  • Help identify slow changes occurring over the course of multiple chest X-rays that might otherwise be overlooked;
  • Benefit patients in developing countries that do not have access to radiologists to read their chest X-rays; and 
  • Create a virtual radiology resident that can later be taught to read more complex images like computed tomography (CT) and magnetic resonance imaging (MRI) in the future.

The NIH research hospital anticipates adding a large dataset of CT scans to be made available as well in the coming months.

For more information: www.clinicalcenter.nih.gov

 

Related Content on Artificial Intelligence in Radiology

Artificial Intelligence Could Learn From the Medical Imaging Goldmine of the NHS Archives

VIDEO: Machine Learning and the Future of Radiology

How Artificial Intelligence Will Change Medical Imaging

Must Radiologists Be Prepared To Delegate ... To Smart Machines?

Related Content

Lung and respiratory health pioneer paves way for more precise care of complex respiratory conditions
News | Artificial Intelligence | September 25, 2020
September 25, 2020 — VIDA Diagnostics, Inc. announced that it has received 510(k) clearance from the Food and Drug Ad
EchoGo Pro automates cardiac ultrasound measurements for heart functions, but also empower physicians to predict the occurrence of coronary artery disease (CAD).

EchoGo Pro automates cardiac ultrasound measurements for heart functions, but also empower physicians to predict the occurrence of coronary artery disease (CAD).

News | Cardiovascular Ultrasound | September 25, 2020
September 25, 2020 — Based on its recent analysis of the global...
RADLogics AI-Powered solution in use: chest X-ray of COVID-19 positive case with heatmap key image.

RADLogics AI-Powered solution in use: chest X-ray of COVID-19 positive case with heatmap key image.

News | Artificial Intelligence | September 23, 2020
September 23, 2020 — RADLogics
The cartilage in this MRI scan of a knee is colorized to show greater contrast between shades of gray.

The cartilage in this MRI scan of a knee is colorized to show greater contrast between shades of gray. Image courtesy of Kundu et al. (2020) PNAS

News | Artificial Intelligence | September 22, 2020
September 22, 2020 — Researchers at the University of Pitts...
Philips Azurion Lung Edition supports high precision diagnosis and minimally invasive therapy in one room
News | Lung Imaging | September 21, 2020
September 21, 2020 — Philips introduced...
Of all the buzzwords one would have guessed would dominate 2020, few expected it to be “virtual”. We have been virtualizing various aspects of our lives for many years, but the circumstances of this one has moved almost all of our lives into the virtual realm.

Getty Images

Feature | Radiology Education | September 18, 2020 | By Jef Williams
Of all the buzzwords one would have guessed would dominate 2020, few expected it to be “virtual”.
As the silos of data and diagnostic imaging PACS systems are being collapsed and secured, the modular enterprise imaging platform approach is gaining significance, offering systemness and security
Feature | Coronavirus (COVID-19) | September 18, 2020 | By Anjum M. Ahmed, M.D., MBBS, MBA, MIS
COVID-19 is now everywhere, and these are the lo
News | Artificial Intelligence | September 16, 2020
September 16, 2020 — Konica Minolta Healthcare Americas, Inc.