Case Study: Medical Imaging
Scaling AI-Driven Diagnostics with a Pan-India Imaging Annotation Platform
A leading healthcare consortium accelerates development of AI-powered diagnostic tools with a unified data annotation platform.
Client Overview
A leading healthcare consortium spanning 12 premier hospitals across India sought to accelerate development of AI-powered diagnostic tools for Ultrasound, CT, and X-ray imaging. Their goal was to create a richly annotated dataset of 2 million medical images—covering a wide range of modalities and pathologies—to train and validate advanced computer-aided diagnostic (CAD) algorithms.
The Challenge
- Massive Annotation Volume: Annotating 2 million images demands rigorous coordination, consistent standards, and rapid throughput.
- Distributed Expertise: Subject-matter experts (radiologists, sonographers, technicians) were spread across 12 facilities, each using different local workflows and tools.
- Quality & Compliance: Ensuring annotation accuracy and HIPAA-equivalent privacy controls across multiple sites was critical for clinical credibility and regulatory approval.
Our Solution: Unified AI Training & Data Management Platform
We deployed our cloud-native platform to orchestrate the end-to-end annotation lifecycle:
- Centralized Project Dashboard: Real-time tracking of annotation progress, per-site throughput, and quality metrics. Role-based access controls ensured each expert saw only approved cases and maintained patient privacy.
- Custom Annotation Workflows: Pre-configured templates for segmentation, bounding-box marking, and lesion classification across Ultrasound, CT, and X-ray exams. Integrated consensus review loops to resolve discrepancies.
- Expert Pool Management: Onboarded 150+ in-house specialists from 12 hospitals via single sign-on. Automated task assignment balanced workload and leveraged each expert’s strengths.
- Automated Quality Assurance: Built-in validation checks flagged outlier annotations and inter-annotator variance beyond 5%. Continuous feedback guided experts to maintain > 98% accuracy.
Implementation & Timeline
| Phase | Duration | Key Activities |
|---|---|---|
| Platform Onboarding | 2 weeks | Environment setup, user training, annotation-task design |
| Pilot Annotation | 1 month | 50k images annotated, QA calibration, workflow refinement |
| Scale-Up | 6 months | Rolling annotation across all 12 sites, monthly QA audits |
| Dataset Finalization | 1 month | Consolidation of 2M annotations, final consistency checks |
Results & Impact
- 2 Million Images Annotated in just 8 months, covering key use cases in abdominal ultrasound, thoracic CT, and orthopedic X-ray.
- > 98% Annotation Accuracy against expert-verified benchmarks, ensuring clinical-grade dataset quality.
- 30% Faster Throughput versus projected timelines through automated task balancing and real-time progress monitoring.
- Seamless Collaboration across 12 hospitals, unified under one secure platform—eliminating data silos and streamlining expert workflows.
These annotated datasets powered next-generation CAD models that achieved:
- 94% Sensitivity for lesion detection in ultrasound.
- 92% Specificity for pulmonary nodule classification in CT.
- 90% Accuracy in fracture identification on X-ray.
Conclusion
By leveraging our AI training and data management platform, the consortium transformed a complex, distributed annotation challenge into a streamlined, high-quality process—enabling the rapid development of AI algorithms that are now poised to redefine diagnostic workflows across India.
Ready to scale your medical imaging AI projects? Contact us to learn how our platform can accelerate your data annotation, ensure quality at scale, and drive breakthrough outcomes in computer-aided diagnostics.