Breast-SlimView

Hera-MI

Breast-SlimView is a clinical decision support solution for 2D/3D mammography. It has learnt to detect what is normal from a database of mammograms, then, based on this normalized database, it learns to detect and highlight suspicious areas. Breast-SlimView’s algorithm automatically detects and removes all normal physiological areas (vessels, glandular tissue, fatty tissue and mammary gland) and replace them by artificial fat.
Product specifications Information source: Vendor
Last updated: Aug. 3, 2020
General
Product name Breast-SlimView
Company Hera-MI
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features Images negativation, risk mapping, breast density.
Suggested use Before: stratifying reading process (non, single, double read)
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand)
After: diagnosis verification
Data characteristics
Population Breast cancer screening population
Input 2D & 3D mammography, reports.
Input format DICOM
Output 2D & 3D negativated mammography, density results.
Output format DICOM
Technology
Integration Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker)
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 10 - 60 seconds
Certification
CE Certified, Class IIa
FDA No or not yet
Market presence
On market since 02-2020
Distribution channels
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Pay-per-use, Subscription
Based on Number of users, Number of installations, Number of analyses
Evidence
Peer reviewed papers on performance
Non-peer reviewed papers on performance
Other relevant papers