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COMPUTER VISION
Quality Detection System
Academic Medical Research Institution
99.2%
Detection Accuracy
93%
Fewer False Negatives
-78%
Review Time Per Slide
3x
Clinical Trial Throughput
The Challenge
A leading academic medical center's pathology department was processing 2,400+ tissue samples daily for cancer detection. Manual microscopy review faced significant challenges, including inter-observer variability of 12-18% between pathologists.
Each slide required 15-20 minutes of expert review, creating an 8-hour turnaround time for urgent cases and a growing backlog that threatened research timelines. They needed to augment (not replace) their expert pathologists with AI-assisted screening to maintain quality while increasing throughput.
The Approach
PRR developed a Computer Vision pipeline using custom-trained models:
- Ensemble of YOLOv8 and custom segmentation models trained on 180,000 annotated images
- Integration with 4 different microscopy systems and slide scanners for seamless data ingestion
- Human-in-the-Loop workflow where AI flags regions of interest for pathologist final determination
- Full audit trail and traceability for regulatory compliance
Detection Pipeline
1
Ingestion
Multi-source Microscopy Feed
2
Analysis
YOLOv8 + Segmentation
3
Review
Pathologist Confirmation
The Impact
| Metric | Before | After | Improvement |
|---|---|---|---|
| Detection accuracy | 87% | 99.2% | +12 points |
| False negative rate | 4.2% | 0.3% | 93% reduction |
| Review time per slide | 18 min | 4 min | -78% |
| Urgent case turnaround | 8 hours | 2 hours | -75% |
Research impact: Enabled 3x increase in clinical trial throughput
Tech Stack
YOLOv8
Custom Segmentation Models
Azure Machine Learning
NVIDIA DGX
Azure IoT Edge
HL7 FHIR
Precision AI for high-stakes environments.
Learn how computer vision can augment your experts and improve quality assurance.
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