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Key Features

  • Breast density assessment according to BIRADS 5th edition

General Information

Product name

ClariSIGMAM

Subspeciality

Breast

Modality

Mammography

Disease targeted

Breast cancer

Main task

Not specified

Technical Specifications

Population

Anyone who requires mammography exam

Patient population age

Not specified

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 CIS (Clinical Information System), Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application

Deployment

Cloud-based, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, Etc., Image upload, On demand, Right after the image acquisition, Triggered by a user through e.g. a button click

Processing time

3 - 10 seconds

Regulatory Information

CE Certification

Pathway:

MDD

Class:

Class I

Verified by Health AI Register
FDA Certification

Pathway:

510(k) cleared

Class:

Class II

Verified by Health AI Register

Other certifications

Not specified

Market Presence

On market since

09-2021

AI Platforms

Bayer Pharmaceuticals, Blackford Analysis, Deepc, TeraRecon

Resellers

Not specified

Countries present

1

Paying clinical customers

8

Research/test users

5

Pricing Information

Pricing model

One-time license fee, Pay-per-use, Subscription

Based on

Number of analyses, Number of installations

Evidence & Research

Peer-Reviewed Papers

Peer-Reviewed

View

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

Peer-Reviewed

View

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

Source: vendor | First published: Oct 4, 2023 | Last updated: Jul 9, 2025