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Automated Integration of AI Results into Radiology Reports Using Common Data Elements.

Garv Mehdiratta1, Jeffrey T Duda1, Ameena Elahi2

  • 1Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.

Journal of Imaging Informatics in Medicine
|January 27, 2025
PubMed
Summary
This summary is machine-generated.

Common data elements (CDEs) enable seamless integration of artificial intelligence (AI) measurements into radiology reports. This standardized framework improves data consistency, interoperability, and communication for AI-driven healthcare insights.

Keywords:
Artificial intelligenceCommon data elementsInteroperabilityRadiologyReportingStandards

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology Informatics

Background:

  • Artificial intelligence (AI) offers potential to enhance diagnostic accuracy and workflow efficiency in radiology.
  • Integrating AI-derived measurements into radiology reports requires standardized data formats for seamless incorporation.
  • Common Data Elements (CDEs) provide a standardized framework for interoperable health information.

Purpose of the Study:

  • To describe the application of CDEs for embedding AI-derived measurements into radiology reports.
  • To define a set of CDEs for liver and spleen volume and attenuation measurements.
  • To demonstrate the use of CDEs in clinical practice for AI data transfer.

Main Methods:

  • An AI system segmented liver and spleen on non-contrast CT images.
  • AI-generated measurements were recorded as CDEs using the DICOM-SR framework.
  • Automated systems extracted CDEs, incorporated them into reports, and sent images to PACS.

Main Results:

  • The AI system successfully segmented abdominal organs and generated volume and attenuation measurements.
  • CDE labels and values were successfully extracted from AI data and integrated into radiology reports.
  • The process enabled transmission of AI-generated image series to PACS.

Conclusions:

  • Radiology CDEs provide a practical framework for recording and transferring AI-generated data.
  • This approach enhances communication, facilitates research, and improves decision support systems.
  • CDEs ensure consistency, interoperability, and clarity in reporting AI findings across healthcare systems.