BioScan AI Diagnostic
Computer vision implementation for early-stage anomaly detection with 98.4% accuracy across clinical trials.
Project Overview
BioScan contracted our team to build a high-performance clinical vision scanner. By integrating PyTorch models directly into a secure cloud API gateway, our system parses 3D scan uploads and runs anomaly segmentations within seconds, generating auditable records.
The Challenge
Large 3D medical scans represent heavy files (often exceeding 500MB). Running AI segmentations on these images synchronously caused severe API timeouts and bloated GPU compute costs due to idle queue processes.
Our Technical Solution
We structured an asynchronous data ingestion pipeline. Scan uploads are fed to secure AWS S3 buckets, triggering a Lambda process that puts the image path into an AMQP message queue. GPU worker nodes pull from the queue, execute the segmentations, and push coordinate markers back to the database, notifying the client via Server-Sent Events.
Key Achievements & Results
- Reached 98.4% anomaly detection accuracy across active clinical trials.
- Reduced processing queue delay to less than 4 seconds per scan.
- Decreased GPU server footprint costs by 45% through batch scheduling.
- Achieved full HIPAA-compliant data transit and storage isolation.
“Their engineering team resolved our asynchronous bottlenecks completely. Uploading 3D scans is immediate, and the segmentation feedback feels seamless to clinicians.”
Dr. Karen Schultz
VP of Product, BioScan Labs
Technologies Utilized
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