An innovative DICOM appliance solution developed for Heart Health's groundbreaking cardiac imaging AI platform. This sophisticated system acts as an intelligent bridge between hospital MRI machines and AI-powered cardiac analysis models, enabling automated detection and classification of cardiac anomalies.
The appliance receives cardiac medical images directly from MRI equipment, performs secure patient anonymization while maintaining separate mapping records, and routes the images to advanced AI models for automated analysis. The AI algorithms identify critical cardiac features including endocardium and epicardium boundaries, and detect calcification in heart walls. Results are seamlessly transmitted back to the hospital's PACS system through the same DICOM appliance, creating a complete automated diagnostic workflow.
Built using Python with pydicom and pynetdicom libraries for robust DICOM DIMSE protocol handling, complemented by a Node.js backend for cloud-based study management on Google Cloud Platform.
Heart Health needed a reliable and secure method to integrate their innovative AI cardiac analysis platform with existing hospital infrastructure. The solution required receiving high-volume cardiac imaging data from MRI machines, ensuring patient privacy through anonymization, routing images to AI models for complex cardiac feature detection, and delivering results back to clinical PACS systems.
The challenge involved maintaining DICOM protocol compliance, handling the computational demands of AI processing, managing patient data mapping securely, and creating a seamless workflow that could operate across hospital networks and cloud infrastructure without disrupting existing clinical operations.
DICOM appliance for direct MRI machine connectivity
Automated patient data anonymization with secure mapping
Intelligent image routing to AI analysis models
AI-powered endocardium boundary detection and analysis
AI-powered epicardium boundary detection and analysis
Automated heart wall calcification identification
Bidirectional PACS integration for result delivery
Python-based DICOM protocol implementation
Node.js cloud backend for study management
Google Cloud Platform infrastructure deployment
Our development approach focused on creating a robust DICOM appliance that seamlessly integrates hospital imaging equipment with cloud-based AI models while maintaining strict data security and clinical workflow requirements.
Designed comprehensive DICOM appliance architecture to handle MRI image reception, anonymization, and PACS communication. Implemented DICOM DIMSE protocols using Python, pydicom, and pynetdicom libraries. Architected patient mapping system for secure anonymization, planned AI model integration workflow, and designed Node.js backend for cloud study management on Google Cloud Platform.
Built DICOM appliance core functionality for receiving cardiac images from MRI machines. Implemented robust anonymization engine with secure patient mapping database. Developed image routing system to AI models for endocardium, epicardium, and calcification detection. Created Node.js backend for managing studies in Google Cloud, established bidirectional communication with hospital PACS systems, and integrated AI analysis results delivery workflow.
Performed comprehensive testing with various MRI machine models and DICOM implementations. Validated anonymization accuracy and patient mapping security. Tested AI model integration and result accuracy. Conducted PACS compatibility testing across different vendor systems. Deployed appliance on-premise with cloud backend integration, implemented monitoring and logging systems, and established failover and data integrity protocols.
Successfully deployed an intelligent DICOM appliance that seamlessly connects Heart Health's AI cardiac analysis platform with hospital infrastructure. The solution enables automated cardiac anomaly detection, providing clinicians with AI-generated insights on endocardium and epicardium boundaries and heart wall calcification directly within their existing PACS workflow.
The secure anonymization system ensures patient privacy while maintaining clinical data integrity. The cloud-integrated architecture allows for scalable AI processing while the on-premise appliance ensures reliable hospital network integration, significantly enhancing cardiac diagnostic capabilities and workflow efficiency.
