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TeraRecon Awarded Three Foundational AI Platform Patents

Patent #1: System and Method for Medical Image Interpretation

Highlights of the System and Method for Medical Image Interpretation patent include:

  • An image identification and AI findings engine that leverages new patient imaging and diagnostic report data and interprets the related data.
  • An AI adjustment tracking engine that tracks the physician’s changes and preferences including interpretation status prompting and the ability to easily reject or omit findings from a report.
  • A machine learning engine that adapts itself based on tracked usage data.
  • An AI communication engine that presents findings into an integrated review platform, in a way that they can be interrogated, accepted or modified. This includes a usage security feature that ensures user security information is available within the image data to allow restriction of use including watermarks and embedded metadata

Patent #2: Medical Image Identification and Interpretation

Highlights of the Medical Image Identification and Interpretation Patent include:

  • An image identification engine that leverages new AND past patient imaging data and diagnostic reports.
  • An AI engine assembler to orchestrate multiple AI engines and analyzes the resulting data to influence additional operations and then deliver the desired findings for any particular study.
  • An AI review engine that presents findings in a way that they can be interrogated, adjusted and accepted (or not), within an integrated and single user experience.
  • An archive engine that saves and stores the generated findings.

Patent #3: System and Method for Medical Image Interpretation

Highlights of the System and Method for Medical Image Interpretation patent include:

  • An AI reporting engine that pre-populates the diagnostic report with related measurements as well as captures any physician adjustments to the medical findings and presentation display protocols for the medical image data.
  • A dynamic AI result engine that leverages user modifications to derive a new image that is then reflected in the viewing engine and further reflected in the diagnostic report. Similarly, changes made inside the report will be reflected in the patient images and displayed in the reviewing platform.