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Large-Scale Standardized Image Integration for Secondary Use Research Projects.

Hannes Ulrich1, Michael Anywar1, Benjamin Kinast1

  • 1Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Campus Kiel, Kiel and Lübeck, Schleswig-Holstein, Germany.

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Summary

This study successfully integrated routine medical imaging data into a standardized format for research. Over 6.6 million radiological studies are now available for artificial intelligence and informatics research.

Keywords:
Clinical databig datadata integrationradiology

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Area of Science:

  • Medical Informatics
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Medical imaging is vital for diagnosis and therapy.
  • Imaging data is a valuable resource for developing new informatics and artificial intelligence methods.
  • Standardizing clinical imaging data is essential for research accessibility.

Purpose of the Study:

  • To describe the successful integration of clinical routine imaging data into a standardized research format.
  • To implement an integration flow for medical imaging and radiological reports.
  • To enhance data findability and retrieval for medical informatics research.

Main Methods:

  • Designed and implemented a data integration flow at University Hospital Schleswig-Holstein.
  • Integrated imaging series and radiological reports from primary systems into an openEHR repository.
  • Enriched data with semantic codes using Health Level Seven FHIR (Fast Healthcare Interoperability Resources) for improved retrieval.

Main Results:

  • Successfully implemented a data integration flow for clinical routine imaging.
  • Integrated 6.6 million radiological studies, comprising 29 million image series.
  • Made a large dataset available in a standardized format for medical (informatics) research.

Conclusions:

  • The integration flow enables the use of clinical imaging data for research purposes.
  • Standardized and enriched imaging data significantly improves findability and retrieval.
  • This initiative provides a substantial resource for advancing artificial intelligence and informatics in medicine.