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

The Caption Guidance software uses artificial intelligence to guide users to get optimal cardiac ultrasound images in a point of care ultrasound (POCUS) setting.

The Caption Guidance software uses artificial intelligence to guide users to get optimal cardiac ultrasound images in a point of care ultrasound (POCUS) setting.

News | Artificial Intelligence | February 13, 2020
February 13, 2020 — The U.S.
Varian announced it has received FDA 510(k) clearance for its Ethos therapy, an Adaptive Intelligence solution. Ethos therapy is an artificial intelligence (AI)-driven holistic solution that provides an opportunity to transform cancer care.
News | Image Guided Radiation Therapy (IGRT) | February 11, 2020
February 11, 2020 — Varian announced it has received FDA 510(k) c
PaxeraHealth enterprise imaging, PACS, VNA solutions
News | Enterprise Imaging | February 11, 2020
February 11, 2020 — Enterprise Imaging developer PaxeraHealth
Mammograms of a 49-year-old woman with invasive lobular carcinoma on the right-side breast

Mammograms of a 49-year-old woman with invasive lobular carcinoma on the right-side breast. A small mass with micro-calcifications on the right-side breast was detected correctly by AI with an abnormality score of 96%. This case was recalled by 7 out of 14 radiologists (4 breast radiologists and 3 general radiologists) initially (without AI) and all 14 radiologists recalled this case correctly with the assistance of AI.

News | Artificial Intelligence | February 11, 2020
February 11, 2020 — A new study, published in...
aycan completed the install of a second aycan xray-print solution at Inspira Health in New Jersey
News | X-Ray | February 10, 2020
February 10, 2020 — aycan completed the install of a second...
An example of artificial intelligence (AI) being developed by Hitachi to automatically review and identify nodules on lung CT scans. This is part of a suite of AI apps Hitachi is developing. This example was being shown as a work in progress at RSNA 2019.

An example of artificial intelligence (AI) being developed by Hitachi to automatically review and identify nodules on lung CT scans. This is part of a suite of AI apps Hitachi is developing. This example was being shown as a work in progress at RSNA 2019. Photo by Dave Fornell.

Feature | Artificial Intelligence | February 07, 2020 | Sanjay Parekh, Ph.D. 
February 7, 2020 – At the 2019 Radiological Society...
Sponsored Content | Videos | Artificial Intelligence | February 07, 2020
At RSNA19, GE Healthcare introduced its...
Sponsored Content | Videos | Artificial Intelligence | February 06, 2020
ProFound AI is an FDA-cleared artificial intelligence (AI) system for reading 3-D breast tomosynthesis images.
Infervision’s deep learning medical imaging platform is helping screen patients for the coronavirus in China. It acts as second pair of eyes to identify multiple diseases from one set of chest scans. The artificial intelligence (AI) can provide a complete view of the nodule, including volume and density.

Infervision’s deep learning medical imaging platform is helping screen patients for the coronavirus in China. It acts as second pair of eyes to identify multiple diseases from one set of chest scans. The artificial intelligence (AI) can provide a complete view of the nodule, including volume and density.

News | Artificial Intelligence | February 04, 2020
February 4, 2020 — Since January 2020, the...
While electronic medical record systems have helped consolidate most patient data into one location, medical imaging IT systems has proved to be more difficult to replicate by large EMR vendors. This has made room in the market for third-party radiology IT vendors that allow easy integration with the larger EMRs like Epic and Cerner. This image shows Agfa's enterprise imaging system, leveraging its ability to be accessed anywhere with internet connection and pull images from radiology and surgery.

While electronic medical record systems have helped consolidate most patient data into one location, medical imaging IT systems has proved to be more difficult to replicate by large EMR vendors. This has made room in the market for third-party radiology information system vendors that allow easy integration with the larger EMRs like Epic and Cerner. This image shows Agfa's enterprise imaging system, leveraging its ability to be accessed anywhere with an internet connection and able to pull in images from both radiology and surgery. 

Feature | Enterprise Imaging | February 02, 2020 | Steve Holloway
The growing influence and uptake of electronic medical records (EMRs) in healthcare has driven debate over the future