PROJECT
GOALS
- Develop a scalable platform for AI-assisted cardiovascular image analysis.
- Enable real-time image processing and anomaly detection.
- Design an intuitive UI tailored for medical professionals.
- Ensure compliance with healthcare regulations (HIPAA, GDPR).
- Integrate with hospital PACS systems and electronic health records (EHR).
- Support secure data storage, encryption, and audit trails.
SOLUTIONS
- AI & ML Development: Implemented custom-trained models for image segmentation, anomaly detection, and risk prediction using MRI and CT scans.
- Visualization Engine: Built a custom DICOM viewer with heatmaps, overlays, and measurement tools to assist clinicians in interpreting AI outputs.
- Secure Infrastructure: Deployed on a HIPAA-compliant cloud environment with role-based access, encrypted data pipelines, and logging.
- UX for Healthcare: Designed a responsive dashboard optimized for desktop and tablet use, tailored for cardiologists and radiologists.
- System Integration: Developed APIs and HL7 interfaces to integrate seamlessly with hospital PACS and EHR systems.
- Continuous Learning: Enabled feedback loop from clinicians to further train and improve AI models over time.
TECHNOLOGIES
- Frontend: React, Redux, TypeScript, DICOM Web Viewer
- Backend: Node.js, PostgreSQL, GraphQL
- Infrastructure: AWS (HIPAA Compliant), Docker, Kubernetes, HL7, FHIR
- AI & ML: Python, TensorFlow, PyTorch, OpenCV
RESULTS
- Reduced diagnosis time by up to 30% through AI-assisted imaging.
- Improved anomaly detection accuracy in early-stage cardiovascular conditions.
- Secure, compliant system ready for clinical deployment and certification.
- Enhanced user satisfaction from radiologists and cardiologists due to a tailored workflow.
- Platform ready for multi-center rollout and future expansion into other specialties.