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This study demonstrates seamless data interoperability between the Medical Imaging and Data Resource Center (MIDRC) and other data repositories. This integration enables the creation of multimodal datasets for advancing artificial intelligence in medicine.

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Area of Science:

  • Medical informatics
  • Data science
  • Biomedical imaging

Background:

  • Interoperability is crucial for integrating multimodal datasets from diverse sources.
  • The Medical Imaging and Data Resource Center (MIDRC) prioritizes data commons interoperability.
  • Existing data repositories often face challenges in data sharing and integration.

Purpose of the Study:

  • To demonstrate and validate the interoperability between MIDRC, BioData Catalyst (BDC), and the National Clinical Cohort Collaborative (N3C).
  • To showcase the creation of integrated clinical and imaging patient cohorts from multiple data sources.
  • To provide a framework for utilizing data repository interoperability for AI/ML model development.

Main Methods:

  • Leveraged existing interoperability features of MIDRC, BDC, and N3C.
  • Constructed two example cohorts with matched clinical and imaging data for specific use cases.
  • Assessed cohort representativeness against CDC population statistics using Jensen-Shannon distance.

Main Results:

  • Successfully established interoperability pathways between MIDRC, BDC, and N3C.
  • Created representative multimodal patient cohorts by integrating data from the repositories.
  • Validated the feasibility of using Jensen-Shannon distance for cohort representativeness analysis.

Conclusions:

  • The demonstrated interoperability facilitates the creation of comprehensive multimodal datasets.
  • This approach supports the development of advanced artificial intelligence and machine learning models in healthcare.
  • MIDRC, BDC, and N3C users can benefit from these methods for data integration and analysis.