Dicom Systems De-identifies 5.3 Million Radiology Exams, Demonstrates AI On-Ramp Feature

Heath IT leader Dicom Systems completes large-scale, advanced de-identification of medical imaging data for Hospital for Special Surgery, a global leader in musculoskeletal sciences.
Dicom Systems De-identifies 5.3 Million Radiology Exams, Demonstrates AI On-Ramp Feature

Dicom Systems, Inc. (, a leader in Enterprise Imaging IT, announced the completion of a large-scale de-identification project for Hospital for Special Surgery (“HSS”) in New York, NY, the global leader in musculoskeletal health and the leading orthopedic hospital in the U.S.

The HSS Global Innovation Institute, which collaborates with industry, investors, and entrepreneurs to commercialize their portfolio of technologies, has partnered with an AI company to develop algorithms targeted to address the issue of fracture misdiagnosis. De-identification played a critical role in unlocking the hospital’s vast archive of studies. With proven technology verified by an independent third party auditor, Dicom Systems delivered the successful de-identification of 5.3 million exams to HSS.

“We found the Dicom Systems Unifier’s de-identification capabilities to be the enterprise-class platform we needed to tackle the complexity of our requirements. This data pool is already in the hands of our AI partner, with the aim to significantly improve diagnostic accuracy of fractures and pathologies in radiology,” said David King, Executive Director for HSS Global Innovation Institute.

“Dicom Systems is proud to work with a world-class institution like HSS to open up their rich pool of data for biomedical research,” said Dmitriy Tochilnik, President and CTO of Dicom Systems. “Our customers know us for offering valuable Enterprise Imaging tools that solve their most immediate problems. When it comes to AI, we offer real-world access to innovation.”

Clinical Data Achieved with Dicom Systems De-Identification

  • Proprietary framework takes HIPAA Privacy Rule, Safe Harbor methodology compliance to a new level
  • Full customization of de-identification processes and output
  • Advanced pixel-level de-identification while avoiding corruption or truncation of the image file
  • When deployed in conjunction with Dicom Systems Enterprise Imaging Unifier, leverages robust framework for imaging lifecycle management and archiving.

“This feature comes on the heels of our announcement to focus on the supply side of imaging data for research, clinical trials and deep learning. Machine learning in medical imaging is a voracious process that requires massive data consumption. Our flagship Unifier platform is particularly well suited for the task,” said Florent Saint-Clair, Executive Vice President of Dicom Systems. “We’re excited to serve as the on-ramp to AI for pioneering clinical and research organizations who are pushing the limits of healthcare innovation.”

De-identification is now available for direct licensing and as an add-on to Dicom Systems Enterprise Imaging Unifier, a platform designed for interoperability, workflow, archiving, cloud and AI on-ramp. A recent recipient of the Google Cloud Partner Award for Innovative Solution for Health Care, Dicom Systems will be presenting at the Third Annual SIIM Conference on Machine Intelligence in Medical Informatics (C-MIMI) September 9-10, 2018 in San Francisco, CA.

About Dicom Systems 

Dicom Systems provides Enterprise Imaging and teleradiology workflow solutions that simplify IT. The features-rich Unifier platform delivers intelligent routing, SSL-based DICOM and HL7 integration tools, DICOM Modality Worklist, and archiving through on-premise, private cloud and leading cloud providers like Google Cloud. Proven at worldwide deployments, Dicom Systems is recognized by top healthcare enterprises, government agencies and partners for next-generation Enterprise Imaging. To learn more, visit

For more information contact Taylur Ngo, Marketing Manager, 415-684-8790,

SOURCE Dicom Systems, Inc.

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