AI Integration for Sectra Neurology Imaging

AI integration for Sectra Neurology focuses on two primary workflow surfaces: the reading worklist and the reporting interface. Within the worklist, AI models can be triggered via DICOM or HL7 messages upon study completion to analyze incoming MRI and CT brain studies. The goal is to prioritize the radiologist's list by flagging studies with potential critical findings—such as intracranial hemorrhage (ICH), mass effect, midline shift, or large vessel occlusion—and moving them to the top. This is achieved by connecting an AI inference service to Sectra's Enterprise Imaging Workflow Orchestrator, which can re-prioritize studies based on AI-generated urgency scores before they hit the neuroradiologist's queue.

During the active reading session, AI results are delivered as DICOM Structured Reports (SR) or via a custom sidebar panel within the Sectra viewer. For quantitative follow-up analysis—common in multiple sclerosis (MS) or dementia workups—AI tools for lesion segmentation and brain volumetry can be embedded. These tools generate precise measurements (e.g., lesion load, hippocampal volume) that are automatically populated into structured report templates, saving the neuroradiologist from manual contouring and calculation. The integration ensures AI outputs are non-destructive overlays, requiring explicit radiologist verification and sign-off before becoming part of the permanent record.

A production rollout typically uses a gateway architecture, where a lightweight service listens for incoming studies from Sectra PACS, routes images to the appropriate AI model (e.g., a stroke detection model for non-contrast head CTs, an MS lesion model for brain MRIs), and posts results back to a designated Sectra node. Governance is critical: all AI interactions are logged with study UIDs, model versions, and inference timestamps for audit trails. A phased implementation starts with a silent mode, where AI runs in the background to build confidence, followed by a pilot where alerts are shown to a subset of users, before full deployment with integrated reporting support.

The integration connects at three primary points within the Sectra Neurology PACS ecosystem: 1) The Worklist Manager API for study prioritization, 2) The DICOM Store SCP for receiving brain MRI/CT studies, and 3) The Reporting Module for injecting AI findings. A secure listener service monitors the DICOM node for new neurology studies tagged with specific modalities (e.g., MR BRAIN, CT HEAD). Upon receipt, studies are anonymized, pre-processed (e.g., NIFTI conversion, skull-stripping), and queued for inference against configured AI models—such as those for intracranial hemorrhage (ICH) detection, large vessel occlusion (LVO) scoring, or quantitative brain volumetry.

Results are returned as DICOM Structured Reports (SR) or HL7 FHIR Observation resources, containing lesion coordinates, confidence scores, and volumetric measurements. For critical findings like a high-confidence ICH, the system can trigger an HL7 ADT^A31 message to update the patient's acuity flag in the EHR and push a priority alert to the Sectra Worklist, moving the case to the top of a neuroradiologist's queue. For quantitative follow-up, AI-generated metrics (e.g., hippocampal volume, white matter hyperintensity load) are written back to the Sectra VNA as secondary captures, enabling side-by-side comparison in the Sectra 3D viewer.

Governance is enforced through a model registry and audit trail that logs every inference, including the source study UID, model version, processing latency, and user who acted on the result. Rollout follows a phased validation: starting with silent mode (AI runs in background, no alerts), progressing to concurrent read (AI results visible as a non-interruptive overlay), and finally to triage mode for after-hours and emergency department support. This architecture ensures AI augments—rather than disrupts—the established Sectra neurology reading protocol, providing a safety net for critical findings and quantitative support for complex degenerative cases.

A production-grade integration for Sectra Neurology Imaging is built on a zero-trust data pipeline. DICOM studies are ingested via Sectra's PACS Gateway or DICOMweb API into a secure, isolated processing environment. AI inference—for tasks like hemorrhage detection, mass effect scoring, or brain volumetry—occurs within this enclave, with results formatted as DICOM Structured Reports (SR) or HL7 FHIR Observations. These AI-generated findings are then injected back into the Sectra workflow via its Enterprise Imaging SDK or a dedicated results listener service, appearing as annotated overlays in the viewer or structured data in the reporting module. All data remains within the health system's compliance boundary, with full audit logging of every study accessed, processed, and returned.

Governance is enforced through role-based access controls (RBAC) within Sectra and the AI orchestration layer. For example, AI prioritization flags might be visible only to neuroradiologists, while quantitative atrophy reports could be auto-routed to neurologists. A human-in-the-loop approval step is configured for critical AI findings (e.g., large vessel occlusion) before final report sign-off, ensuring the radiologist retains diagnostic authority. This model creates a clear audit trail linking the original MRI/CT, the AI algorithm version, the inference result, and the radiologist's final interpretation, which is essential for regulatory compliance and clinical validation.

We recommend a phased rollout to de-risk adoption and build clinical trust. Phase 1 (Silent Mode): AI runs in the background on all neurology studies, but results are logged without affecting the worklist or viewer. This establishes a performance baseline. Phase 2 (Triage Assist): AI prioritization is enabled for the reading worklist, pushing studies with suspected critical findings (like ICH) to the top, but all findings remain non-binding. Phase 3 (Integrated Reporting): AI-generated measurements and draft findings are embedded into the Sectra reporting interface as suggestions, with one-click acceptance into the final report. Each phase includes structured feedback collection and model performance monitoring via tools like our AI Governance and LLMOps Platforms integrations to detect drift and ensure sustained clinical utility.