ClariSIGMAM

AI-powered breast density measurement solution.

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Overview

ClariSIGMAM is a software application intended for use with compatible full field digital mammography systems. It calculates the percentage of breast density defined as the ratio of fibroglandular tissue to total breast area estimates.

Uses this numerical value to provide breast density group information (BI-RADS A and B as fatty and BI-RADS C and D as dense) to aid interpreting physicians in the assessment of breast tissue composition. ClariSIGMAM produces adjunctive information and is not a diagnostic aid.

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Features

  • Fast processing speed promoting efficient and streamlined workflow.
  • DICOM compliant for seamless integration with existing mammogram equipment and PACS.
  • Easy to read report of patient’s mammogram with attributes from craniocaudal (CC) and mediolateral oblique (MLO) view.
  • Provides quantitative and qualitative descriptions of breast density assessment. 

 

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Benefits

  • Eliminates inconsistent results of breast density due to perceptual bias, variation in monitor illumination and different software settings1.
  • Results in consistency and reproducibility2. 
  • Provides objective standards for breast density determination3.
  • Quantitative and qualitative descriptions of breast density assessment.
  • Valuable tool for notifying and educating patient on additional supplementary screenings to improve cancer detection3.
  • Aims to speeds up breast density calculation compared to manual calculation.

 

Image examples

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To find out more about this solution, or the 100+ other applications on the Blackford Platform, book a discovery call with our team.

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We’d welcome the opportunity to learn more about your AI needs and to explain how partnering with Blackford can drive efficiency and provide ongoing value.

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1Kang E, Lee EJ, Jang M, Kim SM, Kim Y, Chun M, et al. Reliability of Computer-Assisted Breast Density Estimation: Comparison of Interactive Thresholding, Semiautomated, and Fully Automated Methods. American Journal of Roentgenology. 2016 Jul;207(1):126–34.

2Kim Y, Hong BW, Kim SJ, Kim JH. A population-based tissue probability map-driven level set method for fully automated mammographic density estimations. Medical Physics. 2014 Jun 10;41(7):071905.

3Ahn C, Heo C, Jin H, Jong Min Kim. A novel deep learning-based approach to high accuracy breast density estimation in digital mammography. 2017 Mar 3;