AI-Driven Mammography Analytics for
Enhanced Breast Cancer Detection
Client Background
The client is an innovative digital pathology startup focused on AI-powered cancer risk assessment with two commercialized products already transforming patient care. Their goal is to transform breast cancer diagnostics by developing a morphology-driven AI platform that predicts recurrence risk from standard H&E slides, enabling personalized treatment plans. With a team of oncologists and data scientists, they seek a technical partner to build a scalable, FDA-cleared diagnostic tool.
Key objectives:
- Develop an AI model to analyze H&E slides for early-stage invasive breast cancer recurrence risk
- Ensure the platform complies with regulatory standards (FDA/CLIA) for clinical use
- Integrate explainable AI to provide clinicians with interpretable insights
Challenge
The company had groundbreaking research but lacked the technical infrastructure to deploy their AI at scale. The prototype struggled with variability in slide quality, low interoperability with hospital systems, and black-box AI that clinicians distrusted. They needed a secure, production-ready platform that could:
- Inconsistent slide quality across scanners and staining protocols required normalization
- Standardize slide analysis across diverse scanners and staining protocols
- Scale computationally to process millions of data points per patient
- Pass regulatory audits with rigorous validation and traceability
- Deliver actionable reports (not just predictions) to pathologists
Solution
We designed a cloud-based AI diagnostics platform that ingests H&E slides, extracts morphology features, and generates patient-specific risk scores with clinical rationale. The development included:
- Multi-scanner normalization: Preprocessing pipeline to harmonize slide artifacts (blur, stains, etc.)
- Hybrid AI architecture: Combines CNNs for feature extraction and graph-based ML for risk modeling
- Regulatory-ready infrastructure: Audit trails, HIPAA/GDPR compliance, and FDA 21 CFR Part 11 support
- Clinician UI: Interactive dashboards highlighting high-risk regions and confidence scores
Key Features
The software is now deployed across 20+ labs, with ongoing trials for prostate cancer adaptation. Highlights include:
- Morphology Feature Engine: Patented algorithm quantifying 500+ tissue biomarkers
- Dynamic Risk Stratification: Adjusts predictions based on clinical history (e.g., BRCA status)
- API integrations: Seamless EHR/LIS compatibility (Epic, Cerner, etc.)
- Real-time QC Flags: Alerts for poor slide quality
Business Value
Faster, More Accurate DiagnosesReduced pathologist workload by 40% via AI triage of low-risk cases.
Regulatory & Commercial ScalabilityAchieved FDA De Novo clearance in 18 months, with modular design for new cancer types.
Improved Patient Outcomes30% increase in early-stage recurrence detection in validation studies.
Tech Stack:
Python
AWS S3/EC2
HL7/FHIR APIs
Docker
Terraform
GxP validation
React
DICOM viewers
AI
Cloud Analytics
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