Enhancing breast cancer screening research through computational mammography analysis.
The Dense Breast Tissue Challenge
Dense breast tissue affects nearly 50% of women undergoing mammography screening. This tissue type presents a dual challenge: dense tissue both increases cancer risk and may obscure visualization on traditional mammograms, potentially making detection more difficult during screening.
When cancers in dense tissue are not detected during routine screening, they may be found at later stages when treatment is more complex and outcomes are less favorable. Current supplemental imaging methods like MRI or ultrasound can help, but they are typically reserved for women identified as high-risk and may not be accessible to all patients who could potentially benefit.
Beyond Black Box AI: A Biophysics-Driven Approach
Current AI models employ opaque algorithms developed from computational science. Our biophysics-based approach takes a different path, grounding itself in fundamental principles of cancer biology and physics. Our patented approach aims to quantitatively categorize tissue regions based on their spatial organization patterns – a transparent, scientifically grounded method that clinicians can understand and interpret.
We are researching methods to subtype mammographic dense tissue into three categories: fatty tissue (anti-correlated), passive dense tissue (positively correlated), and at-risk active dense tissue (uncorrelated). This distinction is based on research suggesting that tumorigenesis itself follows chaotic, uncorrelated patterns – potentially allowing identification of areas where cancer development may be more likely.
How Our Technology Works
WAVED’s software is being developed to integrate with existing mammography systems as an add-on analysis tool. Our transparent methodology aims to allow radiologists to understand exactly how tissue analysis is generated:
- Measure spatial organization: Quantify hierarchical patterns in tissue structure using validated biophysical principles
- Subtype tissue regions: Categorize areas as at-risk active dense tissue, passive dense tissue, or fatty tissue based on correlation patterns
- Generate visual analysis maps: Develop color-coded overlays showing tissue subtypes that radiologists may be able to interpret and explain to patients
This interpretable approach aims to address a critical barrier preventing AI adoption in clinical practice: lack of clinician trust. When radiologists can understand and explain why additional screening may be recommended, they may gain confidence to make more informed recommendations – especially for the underserved “middle-tier risk” population.
Potential Clinical Impact
For Radiologists
- Additional quantitative data that may support clinical decision-making
- Computational analysis tools for dense breast tissue evaluation
- Additional information that may assist in identifying candidates for supplemental imaging
For Patients
- Potential for more personalized screening recommendations based on tissue analysis
- Technology aimed at identifying those who may benefit from additional imaging
- Research focused on improving analysis of dense breast tissue
Development Status
WAVED Medical is currently in the research and development phase. Our technology has not yet received FDA clearance or approval and is not available for clinical use. We are working toward:
- Completing additional validation studies
- Preparing for FDA regulatory submission
- Establishing partnerships with healthcare institutions for clinical evaluation
For updates on our development progress, please contact us.
Ready to Learn More?
WAVED represents a paradigm shift from black box AI to interpretable, biophysics-driven breast cancer research by quantifying the physical properties of breast tissue itself – providing the foundation for transparent, explainable analysis that clinicians can understand and trust.