AI Integration for Sectra Cloud PACS

AI integrates into Sectra Cloud PACS at three primary architectural layers: the data ingestion pipeline, the clinical workflow engine, and the reporting and communication layer. At the ingestion layer, AI services connect via DICOMweb and HL7 FHIR APIs to intercept incoming studies from modalities or the VNA. This enables immediate, serverless AI inference for tasks like critical finding triage (e.g., pneumothorax, ICH) or automated protocoling before a study hits the radiologist's worklist. Results are written back as DICOM Structured Reports (SR) or HL7 observations, enriching the study metadata within Sectra's vendor-neutral archive.

Within the clinical workflow, AI insights are surfaced contextually. For the radiologist, this means prioritized worklists in the Sectra IDS7 reading station, where AI-flagged urgent cases are elevated. During review, AI-generated annotations—like lesion segmentations or measurement suggestions—can be overlaid as a non-destructive layer on the viewer, adhering to the same hanging protocols. For operational staff, AI-driven analytics can feed into the Sectra Workflow Manager to predict bottlenecks or optimize resource scheduling. This human-in-the-loop design ensures the radiologist remains in control, using AI as a copilot to reduce cognitive load and minimize oversight risk.

Rollout requires a phased, governance-first approach. Start with a single, high-impact use case like chest X-ray triage in the emergency department workflow. Deploy a containerized AI model in a secure cloud tenant (AWS/Azure) adjacent to the Sectra Cloud PACS instance, using zero-trust networking principles. Implement a feedback loop where radiologist corrections to AI suggestions are logged via audit trail and used for continuous model retraining. Scale horizontally by adding new AI applications for different modalities (CT, MRI) and specialties (neurology, mammography), managed through a central AI Orchestrator that handles model versioning, licensing, and result reconciliation. This approach de-risks adoption and builds institutional trust, turning Sectra Cloud PACS into an intelligent, learning platform.

For a deeper dive into the technical patterns for study triage and alerting, see our guide on AI Integration for Radiology Study Triage and Prioritization. To understand how to architect these cloud-native pipelines, review our blueprint for AI Integration for Cloud-Based PACS AI.

A cloud-native integration for Sectra Cloud PACS is built on a secure, event-driven pipeline. The core flow begins when a new study arrives in the Sectra VNA (Vendor Neutral Archive). A DICOMweb listener or a configured HL7 ORM/ORU message triggers an event, securely pushing study metadata to a dedicated ingestion queue (e.g., AWS SQS, Azure Service Bus). This queue decouples the PACS from the AI processing layer, ensuring the clinical system's performance remains unaffected. The AI orchestration service picks up the job, retrieves the anonymized DICOM series via DICOMweb WADO-RS from the Sectra Cloud, and routes it to the appropriate containerized AI inference service—hosted on a managed Kubernetes service like AKS or EKS with GPU nodes for compute-intensive models.

Results are returned as structured DICOM SR (Structured Reports) or JSON, containing findings, confidence scores, and relevant measurements. These are immediately sent back to the Sectra Cloud PACS via DICOMweb STOW-RS, attaching the AI results directly to the original study as a secondary capture or SR series. For urgent findings, the system can also trigger real-time alerts through the Sectra Workflow Manager API or push HL7 ADT/A01 messages to the EHR. The entire pipeline is governed by infrastructure-as-code (Terraform, CloudFormation), with strict IAM roles, encrypted data in transit and at rest, and comprehensive audit logging for HIPAA/GDPR compliance. This architecture enables horizontal scaling to handle fluctuating study volumes and facilitates A/B testing or canary deployments of new AI models without disrupting clinical operations.

Rollout follows a phased governance model. Initial pilots connect AI to non-critical, high-volume workflows like chest X-ray triage or incidental finding detection. AI results are presented as a non-interruptive overlay in the Sectra IDS7 viewer, allowing radiologists to provide feedback that is logged to a dedicated model performance database. This feedback loop, managed through tools like Weights & Biases or Arize, is critical for validation and continuous model improvement before expanding to more complex, diagnostic-use cases. The final architecture ensures AI operates as a seamless, governed co-pilot within the radiologist's native workflow, augmenting efficiency without altering their primary diagnostic interaction with the PACS.

Integrating AI into a clinical imaging workflow requires a security-first architecture and clear operational governance. For Sectra Cloud PACS, this means establishing a zero-trust data pipeline where DICOM studies are never permanently stored outside the PACS. We implement secure, tokenized retrieval via Sectra's DICOMweb API, passing anonymized or pseudonymized image series to a dedicated, isolated inference service—often deployed within your own VPC or Azure tenant. AI-generated findings are returned as DICOM Structured Reports (SR) or via a secure REST webhook, ensuring all AI outputs are stored as first-class DICOM objects within the Sectra archive for full auditability and traceability.

Governance is enforced at multiple layers: Role-Based Access Control (RBAC) within Sectra determines which radiologists or groups see AI prompts and overlays. A configurable confidence threshold allows administrators to control when AI findings are presented as alerts versus subtle cues. Every AI interaction is logged, creating an immutable audit trail that links the original study, the AI model version, the inference result, and the radiologist's final action (accepted, modified, or rejected). This is critical for regulatory compliance (FDA SaMD, CE Mark, HIPAA) and for continuous model validation and improvement through feedback loops.

A phased rollout minimizes disruption and builds trust. Phase 1 often targets a single, high-volume study type (e.g., chest X-rays for pneumothorax detection) in a silent mode, where AI runs in the background and results are logged but not displayed, establishing a performance baseline. Phase 2 introduces non-interruptive notifications within the Sectra viewer, such as a sidebar panel with AI findings for the radiologist to optionally review. Phase 3 enables context-aware overlays and prioritization, where the worklist is intelligently sorted and critical findings are gently highlighted. Each phase is accompanied by change management, tailored training modules within the Sectra environment, and defined metrics for measuring impact on report turnaround time, discrepancy rates, and clinician satisfaction.