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Vara
Vara
Vara (MX Healthcare GmbH)
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).
Information source:
Vendor
Last updated:
June 23, 2022
General Information
Technical Specifications
Regulatory
Market
Evidence
General Information
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
Suggested use
Before: stratifying reading process (non, single, double read), adapting worklist order
During: report suggestion
After: diagnosis verification
Without interference of a radiologist: AI-only diagnosis
Technical Specifications
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
Regulatory
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
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
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