AI Integration for Sectra Veterinary Imaging

AI integration for Sectra Veterinary Imaging connects at three primary points: the worklist, the viewer, and the reporting module. For the worklist, AI algorithms analyze incoming DICOM studies (X-ray, CT, MRI) to prioritize cases—flagging potential fractures in equine limbs or thoracic effusions in canine studies—and reorder the reading queue to surface critical findings first. Within the Sectra viewer, AI results are presented as structured overlays: bounding boxes around suspected lesions on a spinal radiograph, automated measurements of cardiac silhouettes, or segmentation masks highlighting abnormal abdominal organ morphology. These annotations are rendered as DICOM Structured Reports (SR) or via a sidecar overlay, ensuring they are non-destructive to the original images and can be toggled on/off during review.

The practical impact is measured in workflow efficiency and diagnostic confidence. For a mixed animal practice, an AI model trained on canine and feline anatomy can automatically label vertebrae on spinal studies, calculate joint angles for dysplasia screening, and detect subtle pulmonary nodules, reducing manual measurement time from minutes to seconds. For equine or bovine imaging, species-specific models assist in identifying complex fracture patterns in distal limbs or quantifying gas shadows in colic cases. These AI-derived findings are then seamlessly pushed into the Sectra reporting interface, where they can auto-populate draft reports, suggest differential diagnoses based on the visualized anatomy and detected anomalies, and pull in relevant prior studies for comparison. This creates a closed-loop workflow where the radiologist verifies, adjusts, and signs off on an AI-assisted report, with all actions logged for audit and model feedback.

A production rollout requires a phased approach, starting with a single, high-value use case like thoracic radiograph triage in a canine emergency setting. Implementation involves standing up a secure inference service—often containerized on-premises or in a compliant cloud—that receives studies via DICOM send from the Sectra PACS, processes them, and returns results via DICOM SR or a REST API back to the Sectra platform. Governance is critical; results must be clearly marked as 'AI suggestions' requiring radiologist verification, and a feedback mechanism should be established to log radiologist corrections, which are used for continuous model validation and improvement. This architecture ensures AI augments the specialist's expertise without disrupting the familiar Sectra workflow, turning a powerful diagnostic tool into a reliable clinical copilot.

Integration begins at the Sectra Veterinary PACS workstation or worklist manager. Upon study completion (e.g., a canine thoracic radiograph series), the PACS can push the DICOM study to a secure, on-premises or cloud-based AI inference gateway via DICOM C-STORE or a RESTful API. This gateway manages authentication, de-identification if required for external AI models, and orchestrates the routing of studies to the appropriate AI algorithms—such as a fracture detection model for orthopedics or a species-specific anatomy segmentation model for abdominal studies. Results, including bounding boxes, confidence scores, and quantitative measurements, are packaged as DICOM Structured Reports (SR) or a lightweight JSON payload and sent back to the Sectra PACS.

Within Sectra, the AI results are ingested and made available to the veterinary radiologist or clinician in two key ways. First, as non-obtrusive overlays and annotations directly on the images in the viewer, allowing for rapid verification. Second, as structured data populating a side-panel or report template, suggesting findings like "moderate cardiomegaly suggested" or "potential slab fracture at L1." This integration is designed for a human-in-the-loop workflow; the AI acts as a copilot, highlighting areas of interest while the final diagnosis and report remain under the veterinarian's control. For high-throughput clinics, rules can be configured in the Sectra workflow manager to automatically prioritize studies with high-confidence critical findings (e.g., GDV, pneumothorax) at the top of the reading list.

A production rollout requires careful governance. We recommend a phased approach, starting with a single AI application (e.g., feline cardiac silhouette measurement) in a silent mode, where results are logged but not displayed, to establish baseline performance and clinician trust. Post-integration, all AI interactions should be audit-logged within Sectra's system for traceability. For clinics using a Veterinary Practice Management System (VPMS) like ezyVet or IDEXX Neo, a secondary integration layer can push key AI findings (e.g., "osteoarthritis progression noted") and derived billing codes via HL7 or API into the patient's clinical record, closing the loop between diagnostic imaging and practice management. This end-to-edge architecture ensures AI augments the veterinary workflow without disrupting the trusted Sectra environment.

Integrating AI into a Sectra Veterinary Imaging workflow requires strict data governance from the start. This means establishing clear policies for Protected Health Information (PHI) handling, ensuring AI inference occurs on de-identified DICOM studies, and mapping result data back to the original patient record only after processing. All AI-generated findings, such as automated measurements or fracture probability scores, must be written as DICOM Structured Reports (SR) or annotations, creating a permanent, auditable trail within the PACS. Access controls should mirror your existing RBAC (Role-Based Access Control) in Sectra, ensuring only credentialed veterinarians or radiologists can view and approve AI suggestions before they influence patient records or billing.

A phased rollout minimizes disruption and builds trust. Phase 1 (Pilot) typically targets a single, high-volume study type like canine thoracic radiographs. AI runs in a silent mode, generating findings that are saved but not displayed, allowing the practice to audit AI performance against gold-standard reads. Phase 2 (Assistive) enables the AI results as a non-obtrusive overlay or sidebar panel in the Sectra viewer, providing ‘second-look’ support without altering the primary diagnostic workflow. Phase 3 (Integrated) embeds AI triggers into specific hanging protocols or worklists, such as auto-prioritizing studies with a high probability of pathology (e.g., GDV, fractures) for urgent review. Each phase should include defined key performance indicators (KPIs), like reduction in time-to-diagnosis for emergency cases or consistency in measurements for serial orthopedic studies.

Security is paramount when connecting cloud-based AI models to an on-premise or hybrid Sectra deployment. We architect integrations using a zero-trust, API-first model. DICOM studies are sent via secure DICOMweb or TLS-encrypted DICOM send to a dedicated, isolated inference endpoint—often a containerized service within your practice's VPC or a HIPAA-compliant cloud tenant. No PHI is stored in the AI service. The integration should support break-glass workflows where AI services can be instantly disabled without impacting core PACS functionality, and all data transfers must be logged for compliance audits. This controlled, stepwise approach ensures the AI becomes a reliable, governed tool that enhances—rather than risks—your veterinary diagnostic quality and operational efficiency.