WRDensity by Whiterabbit.ai
Overview
WRDensity by Whiterabbit.ai is an AI-driven breast density software designed to assist radiologists in interpreting breast density with greater accuracy and consistency. This standalone software application analyzes ‘for presentation’ data from digital breast x-ray systems, including full-field digital mammography and digital breast tomosynthesis systems, using a deep learning algorithm to assess breast tissue composition.
The primary output of WRDensity is a categorical breast density assessment in accordance with the American College of Radiology (ACR) BI-RADS Atlas 5th Edition breast density categories (A through D). It also provides Breast Density Level Probabilities (BDLP), offering more detailed information about breast density and the device’s confidence level.
WRDensity aims to create uniformity of density assessment at the practice level, delivering highly accurate breast density classification. A longitudinal study in 2022 demonstrated that WRDensity produced more consistent assessments for patients over time compared to radiologists, with 43% more consistency across multiple years. The software was trained on over 600,000 images from the Mallinckrodt Institute of Radiology. Radiologists retain full control, able to easily disagree with the product’s assessment and report breast density based on their own appraisal without additional clicks.
The tool integrates with existing radiology workflows, taking in images via DICOM transfer from mammography imaging systems, PACS, or DICOM routers, and sending outputs to be stored in PACS and RIS for review.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-driven breast density software
- Assists radiologists in interpreting breast density
- Creates uniformity of density assessment at the practice level
- Delivers highly accurate breast density classification
- Provides ACR BI-RADS Atlas 5th Edition breast density categories (A-D)
- Generates Breast Density Level (BDL) and Breast Density Level Probabilities (BDLP)
- Trained on over 600,000 mammography images
- Integrates with PACS and RIS via DICOM transfer
- Allows radiologists to override or confirm assessments
- Demonstrates more longitudinally consistent assessments than radiologists
Use Cases
- Automated breast density assessment in mammography screening
- Improving consistency and standardization of breast density interpretation
- Aiding physicians in the objective assessment of breast tissue composition
- Supporting individualized supplemental screening recommendations for patients
- Enhancing radiologist workflow and productivity in breast imaging
- Reducing unnecessary callbacks and patient anxiety by improving diagnostic accuracy (indirectly)
What Physicians Need to Know
Leverage WRDensity for standardized and consistent breast density assessment, which is crucial for effective patient management and tailoring supplemental screening recommendations. Utilize the tool's objective data to quickly identify breast density levels, especially in challenging cases. Remember that the AI serves as a supporting tool, and your clinical judgment remains paramount, with the ability to easily override its assessment. Consider the benefits of WRDensity's longitudinal consistency for patients undergoing multiple screenings over time.
WRDensity is engineered for seamless integration into existing radiology workflows. It facilitates DICOM image transfer from various mammography systems, PACS, or DICOM routers, and its outputs are designed to be stored in PACS and RIS for convenient review on standard mammography workstations. Its availability on the American College of Radiology's (ACR) AI-LAB platform further underscores its interoperability within the broader radiology ecosystem.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Contact vendor — Contact vendor for customized pricing. |
| Deployment | Standalone software application, integrates with existing RIS/PACS infrastructure. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated WRDensity received 510(k) clearance from the FDA on October 30, 2020, under submission number K202013. It is classified as a Class II medical device (Regulation Number: 21 CFR 892.2050, Product Code: QIH) intended to aid interpreting physicians in the assessment of breast tissue composition by providing an ACR BI-RADS Atlas 5th Edition breast density category. |
| Integrations | |
| EHR | Not specified |
| Specialties | Radiology |
What the Web Says
WRDensity by Whiterabbit.ai is an AI-driven software designed to assist radiologists in consistently assessing breast density, which is a crucial risk factor for breast cancer. The software aims to improve early detection and enhance the quality of patient care by providing accurate breast density classifications. It received FDA clearance in 2020 and has been trained on a large dataset of over 600,000 images from the Mallinckrodt Institute of Radiology.
Overall: PositiveStrengths
- Promotes consistency in breast density assessments among radiologists.
- Aims to facilitate earlier breast cancer detection.
- Enhances patient reassurance through improved consistency and quality of care.
- Utilizes advanced deep learning techniques for accurate breast density classification.
- Trained on a large and reputable dataset of over 600,000 images.
- Radiologists can easily override the AI's assessment if they disagree.
Limitations
- No specific cons were found in the provided search results from physicians, healthcare IT, tech reviewers, Reddit, G2, or Capterra regarding WRDensity by Whiterabbit.ai.
- General concerns about AI in healthcare include the need for rigorous clinical validation and the potential for propagating data-driven errors if not properly overseen.
- Some general Reddit discussions about AI code review tools mention issues with missing nuances and generating questionable or minor issues.
Based on reviews from: Whiterabbit.ai, Indeed.com, G2, Washington University School of Medicine in St. Louis, PR Newswire, AuntMinnie, Slashdot, SecurityWeek, FirstWord HealthTech, Applied Radiology, The Scientist, Reddit
Last updated: 2026-07-19
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