We partner with healthcare institutions to validate AI that analyzes ultrasound images and drafts structured medical reports in seconds — as decision support, always radiologist-reviewed.
5–10s
87–92%
89–94%
$0
An AI assistant that analyzes breast ultrasound images and drafts detailed medical reports in seconds — a "second pair of eyes" that helps radiologists review images more efficiently and consistently.
Current Status
Research & Development Phase
Regulatory Status
Research Use Only
Partnership Benefit
Free System + Support
Breast cancer is the 2nd leading cause of cancer death in women globally. Radiologists review hundreds of ultrasound images daily — time-consuming, mentally exhausting, and subject to human variability. Early detection saves lives, but the current screening process has challenges:
Radiologists analyze hundreds of images daily, leading to fatigue and potential oversights.
Many regions lack specialized radiologists, causing delays in diagnosis.
Inter-observer variability leads to inconsistent interpretations.
15–20 minutes per exam plus extensive report writing.
We've developed an AI system that works like a radiologist's assistant — providing fast, consistent, and detailed analysis to support clinical decision-making.
Every scan first passes a quality gate — poor or uninterpretable images are flagged for a re-scan rather than guessed at. From there, the pipeline runs in three stages, with the diagnosis coming from validated decision logic and the language model only writing it up:
Attention U-Net Segments the ultrasound pixel-by-pixel to locate the lesion and measure it — size, margins, shape, and location — like a radiologist outlining a suspicious area.
Output: Lesion mask plus measurable, structured features.
Vision Transformer classifier Produces a calibrated probability for each class (benign, malignant, normal) with an uncertainty estimate. Validated decision logic — not the language model — maps that probability to a BI-RADS band using a sensitivity-first threshold, and abstains to a radiologist when confidence is low.
Output: Calibrated class + provisional BI-RADS, or an explicit “low confidence → radiologist” flag.
GPT-5 Turns the fixed, structured findings into a professional report using proper radiological terminology. It can explain the decision, but is explicitly forbidden from changing the class or BI-RADS.
Output: Structured report with BI-RADS category and follow-up recommendations.
5–10 seconds
Total processing time per image
Oval, well-circumscribed hypoechoic lesion measuring 1.2 cm. Smooth margins with parallel orientation to skin. Posterior acoustic enhancement present (suggests fluid-filled).
Normal surrounding tissue architecture. No distortion. Skin thickness normal (2 mm).
Normal blood flow pattern. No enlarged lymph nodes visualized.
Findings consistent with simple cyst or fibroadenoma. BI-RADS 2 (Benign). Routine screening in 12 months recommended.
Segmentation model with attention mechanisms that focus on relevant tissue regions and measure each lesion. Trained on 3,200+ annotated images.
A ViT classifier produces calibrated probabilities; validated rules — not the AI — set the BI-RADS band on a sensitivity-first threshold and abstain to a radiologist when unsure.
Language model that writes the report around the fixed decision in professional radiological terminology. It describes the findings; it never sets the diagnosis.
* Based on validation with the BUSI dataset. Real-world performance to be validated through partnership studies.
20–30% reduction in case review time, allowing focus on complex cases.
A standardized approach reduces inter-observer variability.
Independent validation catches potential oversights.
Pre-populated structured reports in proper BI-RADS format.
Process more patients with the same resources.
Built-in quality checks and performance monitoring.
An educational resource for radiology residents and fellows.
Academic publications and conference presentations.
We're seeking clinical research partners to help validate this technology in real-world settings. Together, we'll demonstrate its clinical utility and prepare for regulatory approval.
Initial Setup
1–2 days for installation & training
Weekly Reviews
1–2 hours per week for case review
Monthly Meetings
1 hour for progress updates
This AI is not a substitute for radiologist interpretation. It serves as a clinical decision-support tool only.
What the AI can do
What the AI cannot do
Current Status
Research & Development Phase
Approved Use
Research with institutional approval
Approved for: research studies with institutional approval, educational purposes, and clinical decision support with radiologist verification.
Join healthcare institutions validating this technology. Be among the first to access the system at no cost during the research phase.
Zero Cost
Free system, installation & support
Co-Authorship
Publications & academic recognition
Early Access
Priority for the commercial version
Your data stays secure. Compliant with local regulations. Ethics committee support provided.
We typically respond to partnership inquiries within 24 hours. Our team is glad to discuss how we can collaborate with your institution.
Research Use Only: This AI system is currently under development and validation. It is not approved for standalone clinical diagnosis.
Clinical Responsibility: The interpreting radiologist retains full responsibility for all diagnostic decisions. AI outputs are advisory only.
No Warranties: While we strive for accuracy, AI systems can make errors. Always verify AI findings independently.
Data Privacy: We comply with all applicable data protection regulations. Specific protections are detailed in partnership agreements.