The ClariSIGMAM is a breast density assessment solution that provides density estimates from standard digital mammograms. It automatically analyzes 2D digital mammograms to calculate breast tissue composition. It assesses breast density and generates a breast density grade in line with the American College of Radiology’s BI-RADS density classification scales. The breast density output by ClariSIGMAM is designed to display on mammography workstations or PACS as a DICOM mammography structured report or secondary capture.
Product specifications Information source: Vendor
Last updated: Oct. 4, 2023
Product name ClariSIGMAM
Company ClariPi Inc.
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features Breast density assessment according to BIRADS 5th edition
Suggested use During: interactive decision support (shows abnormalities/results only on demand), report suggestion
Data characteristics
Population Anyone who requires mammography exam
Input Digital mammography images for presentation, RCC, LCC, RMLO, LMLO
Input format DICOM
Output Report for each breast: • Area of fibroglandular tissue (cm²) • Area of breast (cm²) • Area-based breast density (%) For each patient: • Breast density group information for the patient (BI-RADS)
Output format DICOM
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
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 3 - 10 seconds
Certified, Class I , MDD
510(k) cleared, Class II
Market presence
On market since 09-2021
Distribution channels Calantic, Blackford, deepcOS, Eureka Clinical AI
Countries present (clinical, non-research use) 1
Paying clinical customers (institutes) 8
Research/test users (institutes) 5
Pricing model Pay-per-use, Subscription, One-time license fee
Based on Number of installations, Number of analyses
Peer reviewed papers on performance

  • Reliability of Computer-Assisted Breast Density Estimation: Comparison of Interactive Thresholding, Semiautomated, and Fully Automated Methods (read)

  • A novel deep learning-based approach to high accuracy breast density estimation in digital mammography (read)

Non-peer reviewed papers on performance
Other relevant papers