AI Integration for Sectra Enterprise Imaging

AI integration for Sectra is not a single point solution but a layered strategy that connects to its core architectural components. The primary entry points are the Vendor Neutral Archive (VNA) for data access and the Workflow Orchestrator for process automation. AI models can be triggered via DICOMweb services on study arrival in the VNA or through rules in the orchestrator, enabling automated triage and prioritization before a study even hits a radiologist's worklist. For result delivery, AI findings are embedded as DICOM Structured Reports (SR) or HL7 FHIR observations, which are then consumed by the Sectra IDS7 diagnostic workstation, the Sectra Reporting module, or specialty viewers for radiology, pathology, and cardiology.

Implementation follows a hub-and-spoke model: a central AI inference service, hosted on-premises or in a compliant cloud, acts as the hub. It connects to Sectra's APIs and listens for HL7 ADT and ORM messages or monitors DICOM destinations. When a relevant study arrives—like a non-contrast head CT for stroke—the service pulls the images, runs inference, and pushes back an SR with findings (e.g., "ASPECTS score: 8, no large vessel occlusion detected"). This SR is stored in the VNA and can be displayed as an overlay in IDS7, auto-populate report macros, or trigger an alert in the Sectra Workflow Dashboard for immediate case escalation. This keeps the radiologist in the loop while significantly accelerating the critical 'time-to-notification' for emergent findings.

Rollout and governance are critical. A phased approach typically starts with a single, high-impact workflow—such as chest X-ray triage for critical findings in the ER—within one department. This allows for validation of the integration's data pipeline, performance under load, and clinician feedback mechanisms. Governance requires tight collaboration with Sectra's professional services to configure the orchestrator rules, hanging protocols in IDS7 to display AI results, and audit trails to track AI-influenced decisions. Successful scaling depends on establishing a repeatable pattern: containerized AI models, standardized DICOM SR templates, and defined feedback loops where radiologist corrections are used to retrain models, managed through an integrated platform like our AI Governance and LLMOps services.

A robust integration for Sectra connects AI inference services to three primary surfaces: the Workflow Orchestrator for study prioritization, the Vendor Neutral Archive (VNA) for on-demand analysis of stored studies, and the specialty-specific viewer modules (Radiology, Pathology, Cardiology) for contextual result presentation. The core pattern uses DICOMweb services and HL7 v2/FHIR messages to create a secure, event-driven pipeline. When a new study arrives in the VNA, a DICOM STOW-RS operation triggers an event, queuing the study for AI processing. The AI service, hosted in a compliant cloud or on-premises environment, retrieves the study via WADO-RS, executes the model (e.g., for triage, detection, or quantification), and returns structured results as a DICOM Structured Report (SR) or FHIR Observation back to the archive. The Workflow Orchestrator then consumes these results to reprioritize the reading worklist, flagging critical cases for immediate review.

For clinician interaction, AI findings are embedded directly into the diagnostic workflow. In Sectra's IDS7 viewer, results are presented as interactive overlays, measurement annotations, or structured finding lists within the same hanging protocol. This tight integration prevents context switching and allows the radiologist or pathologist to accept, modify, or reject AI suggestions within their native reporting tool, whether it's integrated speech recognition or a structured reporting module. For cross-specialty workflows—such as a cardiology MRI informing a radiology chest CT review—AI-generated metadata (e.g., ejection fraction, lesion coordinates) is written to the VNA as DICOM SR, making it discoverable for correlated studies and enabling a unified patient timeline.

Governance and rollout are managed through Sectra's administrative consoles. AI applications are registered as discrete services with defined RBAC, ensuring only authorized users and workflows can trigger inference. An audit trail logs every AI access, inference result, and clinician interaction for compliance and model performance monitoring. A phased rollout typically starts with a single, high-impact use case like pneumothorax detection on chest X-rays in the Emergency Radiology workflow or mitotic figure counting in Sectra Pathology, deployed in a "second read" silent mode to validate performance and build clinical trust before enabling worklist prioritization or draft report generation.

A production AI integration for Sectra requires a governance-first architecture that aligns with clinical and IT policies. This typically involves:

• Secure Data Pipelines: Using Sectra's Vendor Neutral Archive (VNA) and DICOMweb APIs to create read-only, de-identified data streams for AI inference, with results written back as DICOM Structured Reports (SR) or HL7 messages.
• Role-Based Access Control (RBAC): Integrating AI outputs with Sectra's user and role management, ensuring AI findings are only surfaced to authorized radiologists, cardiologists, or pathologists within their respective modules.
• Audit Trails: Logging all AI inference requests, model versions, and user interactions with AI suggestions within Sectra's native audit systems or a separate LLMOps platform for traceability and compliance.

A phased rollout mitigates risk and builds clinician trust. A common pattern is:

1. Silent Validation Phase: AI runs in the background on historical studies, comparing its outputs to finalized reports without impacting the live workflow. Metrics on accuracy and drift are monitored.
2. Assistive Triage Phase: AI begins to influence the worklist orchestrator in Sectra PACS, prioritizing studies with high-confidence critical findings (e.g., pneumothorax, large vessel occlusion) but requiring radiologist confirmation for all results.
3. Integrated Reporting Phase: AI-generated findings are presented as draft text or structured data within the Sectra Reporting module, with the radiologist acting as editor and final signatory. This phase often starts in a single subspecialty (e.g., chest CT) before expanding.

Security is non-negotiable. The integration architecture must ensure PHI never leaves the health system's controlled environment unless using a Sectra-approved, HIPAA-compliant cloud AI service. For on-premises or private cloud deployments, AI models are containerized and deployed within the hospital's secure network, accessing imaging data through internal APIs. All communication is encrypted, and model weights are secured. A formal change control process for model updates, coupled with ongoing performance monitoring against a golden dataset, ensures the AI integration remains a reliable and safe component of the diagnostic workflow.

Successful governance also involves clear operational protocols. Define when and how radiologists should override AI suggestions, establish a feedback loop to flag incorrect AI outputs for model retraining, and integrate AI performance dashboards into existing quality assurance workflows. This structured, incremental approach transforms AI from a disruptive technology into a governed, scalable asset that enhances the Sectra platform without compromising safety or workflow integrity. For related architectural patterns, see our guides on AI Integration for Vendor Neutral Archives (VNA) and AI Integration for Radiology Reporting Platforms.