AI-Driven Mammography Analytics

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

• 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