Vara's Decision Referral Pathway ensures that each mammogram is analysed by AI and classified into one of three categories: "normal" (AI is confident there are no suspicious signs), "no classification" (AI is not confident about a classification), or "Safety Net" (AI is confident that it is highly suspicious). Vara pre-screens normal mammograms with very high confidence (pre-fills the report), allowing the reader to focus on potentially suspicious exams. Vara also post-screens mammograms with very high confidence for potentially missed exams (only triggered if the reader assigns BI-RADS <3).
Product specifications Information source: Vendor
Last updated: June 23, 2022
General
Product name Vara
Company Vara (MX Healthcare GmbH)
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features Triaging normal exams, safety net, report generation, AI-based workflow
Data characteristics
Population Women of screening age
Input 2D mammograms of a screening study incl. prior images and patient history
Input format DICOM
Output Draft radiology report, segmentation overlay, model score, triaging recommendation, cancer recommendation
Output format Proprietary viewer and worklist, HL7/XML/JSON
Technology
Integration Integration RIS (Radiological Information System), Stand-alone webbased
Deployment Cloud-based
Trigger for analysis Automatically, right after the image acquisition
Processing time
Certification
CE
Certified, Class IIb , MDR
FDA
No or not yet
Intended use statements
Intended use (according to CE) The software aids in assessing of compatible digital or digitized images of the human breast regarding the presence or absence of cancer findings using mathematical methods. It further supports the creation of machine-readable medical reports.
Market presence
On market since 10-2019
Distribution channels
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model
Based on
Evidence
Peer reviewed papers on performance

  • Combining the strengths of radiologists and AI for breast cancer screening : a retrospective analysis (read)

  • AI-based prevention of interval cancers in a national mammography screening program (read)

Non-peer reviewed papers on performance
Other relevant papers