NIH Clinical Center provides one of the largest publicly available chest x-ray datasets to scientific community

NIH Clinical Center sent this bulletin at 09/27/2017 10:00 AM EDT

For Immediate Release
September 27, 2017

Contact:
Molly Freimuth
301-549-5789

NIH Clinical Center provides one of the largest publicly available chest x-ray datasets to scientific community

WHAT:
The 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.

The 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 which 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 CT and MRI in the future.

With an ongoing commitment to data sharing, the NIH research hospital anticipates adding a large dataset of CT scans to be made available as well in the coming months.

ARTICLE:
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR 2017, http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf

Images are available via Box: https://nihcc.app.box.com/v/ChestXray-NIHCC

WHO:
Ronald M. Summers, M.D., Ph.D., Senior Investigator of the Clinical Image Processing Service in the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory of the NIH Clinical Center Radiology and Imaging Sciences Department is available for interviews.

About the NIH Clinical Center: The NIH Clinical Center is the clinical research hospital for the National Institutes of Health. Through clinical research, clinician-investigators translate laboratory discoveries into better treatments, therapies and interventions to improve the nation's health. More information: https://clinicalcenter.nih.gov.

About the National Institutes of Health (NIH):  NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.

 

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