Accelerating Rare Disease Diagnosis with Computer Vision

The Challenge

Pathologists at Global MedTech Solutions were spending thousands of hours manually reviewing high-resolution digital slides. The process was not only expensive and slow, but the cognitive fatigue led to an unacceptable 15% error rate in detecting a specific class of rare cellular anomalies.

They needed a solution that could pre-screen slides, highlight areas of interest, and integrate seamlessly into their existing FDA-approved diagnostic software—without compromising patient data security.

Our Approach

Our engineering team started by establishing a secure, air-gapped data pipeline to anonymize and process over 100,000 historical slides. We then customized and fine-tuned a state-of-the-art Convolutional Neural Network (CNN).

• Implemented a tile-based processing approach to handle multi-gigabyte slide images.
• Utilized active learning to help expert pathologists label the most challenging edge cases.
• Deployed the model using TensorRT on optimized GPU instances for real-time inference.
• Built a custom API gateway compliant with strict healthcare regulations (HIPAA/HITRUST).

"Widle.ai didn't just give us an algorithm; they delivered a production-ready system that our pathologists actually want to use."

Results & Impact

The system is now deployed across 45 regional labs. It acts as a highly accurate 'second pair of eyes,' reducing the average time to diagnosis from 48 hours to under 2 hours. By catching anomalies early and accurately, Global MedTech has improved patient outcomes while saving an estimated $2.4M annually in operational costs.