Medical Imaging Data for Improved Research

Enhancing Research

Medical images are complex, yet very important for research. The need for high-quality medical imaging data is crucial to drive advancements in diagnostics and treatment. ENDEX™ for data standardization and ENCOG™ for data anonymization enables researchers to access superior data while optimizing their resources. 

Why Standardize and Anonymize Medical Imaging Data?

Medical imaging datasets often come from diverse sources with varying formats, resolutions, and metadata structures. This data diversity poses significant challenges for researchers who require consistent and reliable data to extract meaningful insights. Moreover, patient privacy and data protection regulations demand the anonymization of personal health information (PHI) before sharing or analysis. Managing the data diversity in medical imaging poses challenges for researchers, by having consistent data relieves a lot of the pains associated with data variations.

Watch The ENDEX™ Explainer Video

Watch The ENCOG™ Explainer Video

Quality Data for Research

Data standardization empowers researchers to overcome the complexities of medical imaging data and simplify data processing.

  • Correctness: ENDEX will ensure that the image data and metadata align correctly, identifying laterality conflicts, absence or presence of contrast and correcting typos
  • Completeness: ENDEX fills in missing fields with clinically relevant data, configurable by your site to meet your needs
  • Consistency: ENDEX standardizes studies based on Computer Vision and Natural Language review of the study and series, relabeling data to clinically relevant descriptions
  • Timeliness: Enlitic solutions fit into your workflow where you want, so the data is always current and ready when you need it
  • Protected: ENCOG reviews the pixel and metadata and protects PHI regardless of where it is found, automatically

Data Anonymization Solutions For Researchers

The anonymization of data is essential to protect patient privacy. However, traditional anonymization methods often result in the removal of clinically relevant information along with PHI.

ENCOG addresses this issue by intelligently removing PHI while retaining crucial clinically relevant information. This unique approach offers several benefits to researchers:

  • Preservation of Context: Using AI to identify and protect PHI without compromising the contextual information ENCOG can preserve clinically relevant details and researchers can leverage the full potential of the anonymized dataset.
  • Reduced Relabeling Efforts: With ENCOG, researchers can significantly reduce the need relabeling the data and eliminate the laborious and expensive task of manually relabeling the anonymized data.
  • Enhanced Research Efficiency: Researchers can quickly search and find, analyze and interpret anonymized data and streamline workflows. 
  • Anonymization Validation: Ensure effective anonymization with rigorous validation techniques ensuring that no re-identification risks exist within the anonymized dataset.

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