Intended Use

OsteoDetect analyzes wrist radiographs using machine learning techniques to identify and highlight distal radius fractures during the review of posterior-anterior (PA) and lateral (LAT) radiographs of adult wrists.

Technology

OsteoDetect is a software-only device operating in three layers: Network, Presentation, and Decision layers. It accepts CR and DR DICOM images, filters eligible images, preprocesses them, applies a fracture detection deep learning model to analyze for distal radius fractures, then postprocesses results to generate a confidence score and bounding boxes for detected fractures. The output is an annotated DICOM image with fracture markings accessible in PACS.

Performance

The device underwent standalone software validation and a clinical reader study. Standalone testing on 1000 wrist X-rays demonstrated high diagnostic accuracy (AUC 0.965), sensitivity (92.1%), and specificity (90.2%) for distal radius fracture detection. The reader study with 24 clinicians showed OsteoDetect-aided reads had statistically significant improved diagnostic accuracy (AUC 0.889 vs 0.840 unaided, p=0.0056) and better sensitivity/specificity. Testing included localization accuracy and generalizability across patient subgroups and device types.

Device Timeline

  • 1

    Submission

    2/5/2018

    3 months
  • 2

    FDA Approval

    5/24/2018

Other devices from Imagen Technologies, Inc.

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.