Visage Breast Density

by Visage Imaging  · Based in United States → — AI-powered breast density classification for mammography.
Radiology
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Regulatory Status Disclosed

Overview

Visage Breast Density is a software application designed as an add-on module to the Visage 7 Enterprise Imaging Platform. It assesses breast density from mammography studies and provides an ACR BI-RADS Atlas 5th Edition breast density category (A, B, C, or D) to assist radiologists in evaluating breast tissue composition. The tool employs a convolutional neural network (CNN) for automatic classification, with AI inference calculated automatically upon study ingest into PACS, making results available immediately upon study opening.

Visage Breast Density produces adjunctive information and is not intended as a diagnostic aid. It is compatible with full field digital mammography and digital breast tomosynthesis systems, specifically mentioning Hologic equipment. Radiologists can confirm the predicted breast density or adjust the classification.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Automated breast density classification (ACR BI-RADS Atlas 5th Edition)
  • Adjunctive information for radiologists
  • AI inference calculated automatically upon study ingest
  • Integration with Visage 7 Enterprise Imaging Platform
  • Compatible with full field digital mammography and digital breast tomosynthesis systems (e.g., Hologic)
  • Radiologist adjustable classification
  • Confidence score for classification
  • Server-side and client-side components
  • Convolutional Neural Network (CNN) based algorithm
  • DICOM and HL7 standards support for interoperability

Use Cases

  • Aid radiologists in breast tissue composition assessment
  • Standardize breast density classification
  • Guide supplemental breast cancer screening
  • Optimize radiology workflow efficiency
  • Integrate AI algorithms into diagnostic interpretation
  • Enhance consistency in breast density reporting

What Physicians Need to Know

Key Capabilities
Automated breast density classification into ACR BI-RADS Atlas 5th Edition categories (A, B, C, D) with a confidence score. It processes Full Field Digital Mammograms and C-View images from Hologic equipment, utilizing a convolutional neural network (CNN) for classification. AI inference is calculated automatically upon study ingest into PACS, and radiologists can adjust the classification.
Clinical Utility
Aids radiologists in assessing breast tissue composition by providing adjunctive information, not intended as a diagnostic aid. It addresses subjectivity and interobserver variability in visual breast density assessment, supporting compliance with FDA regulations for breast density reporting. The tool has the potential to guide supplemental screening and optimize workflow.
Integration Options
Exclusively available as an add-on module to the Visage 7 Enterprise Imaging Platform, leveraging its infrastructure for image viewing, processing, and archiving. It integrates with reporting software via Visage 7 and displays results through the Visage 7 client. The server-side component integrates with Visage 7 PACS, and the client-side runs on Windows PCs. The Visage AI Accelerator Platform also supports broader interoperability with various AI tools and data formats.
Compliance Status
FDA 510(k) cleared and CE-compliant (Class IIa medical device under EU Regulation 2017/745). It is also registered as a medical device in Europe, Canada, and Australia. Visage Imaging's quality management system complies with EN ISO 13485:2016 (under the MDSAP program).
Pricing Model
Not publicly specified in the available information.
User Experience
Results are available immediately upon study opening, with no dedicated training required for radiologists. Users can confirm or adjust the predicted breast density classification. The tool is designed as an add-on module to the Visage 7 PACS system, with results displayed by the Visage 7 client, aiming for an optimal user experience.
Support Quality
Support is guaranteed within the product lifetime (two years after product discontinuation). Visage Imaging maintains a quality management system with continuous monitoring and improvement through internal and external audits. System administrators collaborate with the Visage engineering team for secure configuration and ongoing security procedures.
Implementation Complexity
Requires the Visage 7 Enterprise Imaging Platform and Hologic imaging equipment. It involves installing server-side and client-side components as modules within the Visage 7 system. Deployment options include cloud-based or locally virtualized solutions. System administrators work with the Visage engineering team for secure configuration and deployment.
Evidence Base
Validated at two large academic centers in North America (Yale and NYU Langone Health). The AI was trained on a large database of mammography exams (e.g., 33,000 studies with 164,000 images). It showed over 80% agreement with expert consensus for four-category BI-RADS classification and over 90% for binary classification (dense vs. non-dense). A post-deployment analysis at Yale reported 99.35% agreement between radiologists and the AI. A peer-reviewed paper on its clinical validation was published in Clinical Imaging (July 2023). It was found substantially equivalent to a predicate device with similar accuracies.
Physician Tip

Visage Breast Density provides an automated, consistent ACR BI-RADS Atlas 5th Edition breast density classification, which can significantly reduce interobserver variability in reporting. While it offers adjunctive information to aid assessment, it is not a diagnostic tool, and radiologists retain the ability to review and adjust the AI's classification. The confidence score can be a valuable indicator of classification certainty. Given the increasing regulatory requirements for breast density reporting, this tool can streamline compliance and potentially guide supplemental screening decisions.

Visage Breast Density is deeply embedded within the Visage 7 Enterprise Imaging Platform, functioning as an add-on module. This tight integration means it leverages Visage 7's robust infrastructure for image management and viewing. It specifically requires input from Hologic imaging equipment. The broader Visage AI Accelerator Platform supports diverse interoperability capabilities (e.g., Open AI API, Python, FHIR), indicating a flexible ecosystem for potential future integrations with other AI tools and data sources within the Visage environment.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Contact for pricing
DeploymentCloud-based, Locally virtualized (virtual machine, Docker), On-premise (as part of Visage 7)
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

Visage Breast Density received FDA 510(k) clearance (K201411) on January 28, 2021. It is classified as a Picture Archiving And Communications System (21 CFR 892.2050) and provides adjunctive information for breast density assessment, not a diagnostic aid.
Integrations
EHR Not specified
Specialties Radiology

What the Web Says

Visage Breast Density is an AI-powered solution designed to objectively and consistently classify breast density from mammograms, addressing the subjectivity and variability of manual assessments. It was developed in partnership with Yale New Haven Hospital and validated at NYU Langone Health, showing high agreement with expert consensus. The tool integrates with the Visage 7 PACS system, providing immediate results to radiologists. This technology is particularly relevant given FDA regulations requiring breast density reporting to patients.

Overall: Positive

Strengths

  • Provides objective and consistent breast density assessment, reducing inter-observer variability among radiologists.
  • Shows high agreement with expert consensus in classifying breast density (over 90% for binary classification and over 80% for four BI-RADS categories).
  • Integrates seamlessly with the Visage 7 PACS system, delivering immediate results to radiologists.
  • Can process Full Field Digital Mammograms and C-View images.
  • No dedicated training is required for radiologists and physicians to use the tool.
  • The AI algorithm can perform more like a consensus group of radiologists than an individual radiologist.

Limitations

  • Some users of the broader Visage 7 platform have reported occasional freezing issues, though this is not specific to the Breast Density module.
  • The system has specific hardware and operating system requirements (Intel 64bit CPU, openSUSE Leap 15.1).
  • Only processes images from validated vendors (currently Hologic, Inc.).
  • Does not include images from prior studies in its assessment.
  • Some physicians, particularly PCPs, may lack awareness or education regarding breast density and supplemental screening recommendations, which could impact patient counseling even with accurate density reporting.
  • The effectiveness of automated breast density measurement software has been questioned in some reviews, citing limited evidence.

Based on reviews from: Visage Imaging (visageimaging.com), G2, Reddit, AuntMinnie, Radiological Society of North America (RSNA), ScienceDaily, The Cancer Letter, The Imaging Wire, PMC (PubMed Central), Densitas, Examined

Last updated: 2026-07-20

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Press & Coverage

Visage Imaging Blog
Visage Breast Density AI Classifier u2013 From Conception to Routine Use
This article details the development, FDA clearance (January 2021), and successful real-world deployment of the Visage Breast Density AI Classifier, highlighting its high agreement with radiologists and potential for optimizing workflow.
2023-08
PR Newswire
Visage Soars with CloudPACS at RSNA 2023
Visage Imaging showcased Visage Breast Density and various AI algorithms at RSNA 2023, emphasizing its integration into diagnostic workflows and participation in the Imaging Artificial Intelligence in Practice (IAIP) demonstration.
2023-11
Clinical Imaging
PACS-integrated machine learning breast density classifier: clinical validation
This peer-reviewed article, co-authored by investigators including those from Yale, validates the high accuracy and agreement of Visage Imaging's PACS-integrated Visage Breast Density application with radiologist assessments for classifying mammography exams into BI-RADS categories.
2023-09
The Imaging Wire
More Work Ahead for Chest X-Ray AI? | ChatGPT for Gen Z
This article mentions a new research study in Clinical Imaging that highlights the high accuracy and agreement of Visage Imaging's PACS-integrated Visage Breast Density application with radiologist assessments for classifying mammography exams.
2023-10
PR Newswire
Visage Leads with CloudPACS at RSNA 2022
Visage Imaging highlighted its FDA-cleared Visage Breast Density algorithm for automated breast density assessment at RSNA 2022, alongside its AI Accelerator program and cloud-based PACS solutions.
2022-11
AJR (American Journal of Roentgenology)
Artificial Intelligence (AI) for Screening Mammography, From the AJR Special Series on AI Applications
This article, published in February 2024, references the FDA approval document for Visage Breast Density from January 29, 2021, as one of the numerous AI applications cleared for breast density assessment.
2024-02
X-ray Interpreter
Visage Breast Density | FDA Radiology AI Device
This entry describes Visage Breast Density as a software application designed to analyze mammography images and classify breast density according to ACR BI-RADS categories.
unknown
accessdata.fda.gov
Traditional 510(k) Summary - Visage Breast Density
This FDA 510(k) summary document details the clearance of Visage Breast Density on January 29, 2021, as a Class II medical device that uses a convolutional neural network for automated breast density classification.
2021-01

Videos

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Frequently Asked Questions

Visage Breast Density integrates with the Visage 7 Enterprise Imaging Platform and Hologic equipment, automatically classifying breast density upon study ingest. It provides an ACR BI-RADS Atlas 5th Edition breast density category as adjunctive information to aid radiologists, who can then confirm or adjust the classification.
Visage Breast Density has FDA 510(k) clearance and CE certification, classifying it as a Class IIa medical device in the EU. This tool aids in standardizing breast density assessment, which is crucial for complying with updated FDA regulations requiring specific breast density information in mammography reports.
Visage Breast Density provides adjunctive information and is not intended as a diagnostic aid; radiologists retain final classification authority. It currently only processes images from Hologic full field digital mammography and digital breast tomosynthesis systems, and requires the Visage 7 Enterprise Imaging Platform.
While Visage Breast Density offers automated, consistent BI-RADS classification, other FDA-cleared AI alternatives like iCAD's PowerLook Density Assessment and Volpara Imaging Software also exist. Key differentiators often include integration capabilities, specific AI algorithms, and performance metrics across diverse datasets.
Specific pricing for Visage Breast Density is not publicly detailed, but it is an add-on module requiring the Visage 7 Enterprise Imaging Platform and Hologic equipment. Therefore, potential costs would include the Visage 7 platform, compatible Hologic equipment, and the specific licensing for the breast density AI module.
Yes, radiologists can easily confirm the predicted breast density or adjust the classification provided by the AI. The system is designed to offer an initial assessment, but the final decision and classification remain with the interpreting radiologist.
Visage Breast Density, like any medical software, should be used in a reasonably secure environment with recognized security standards such as end-point protection, anti-virus software, and appropriate password management. System administrators shall work with the Visage engineering team to review the secure configuration of the environment as part of deployment and to review ongoing security procedures.

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Suggest an Edit → | Last Verified: 2026-04-21 | First Added: 2026-04-21
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