AI Integration for Sectra Ophthalmology

AI integration for Sectra Ophthalmology focuses on three primary functional surfaces: the reading worklist, the image viewer, and the reporting module. The goal is to inject AI-derived insights—such as a DR severity score, glaucoma progression probability, or macular edema quantification—at the point of clinical review without disrupting the radiologist's or ophthalmologist's native workflow. This is achieved by connecting to Sectra's DICOM and HL7 APIs to listen for new ophthalmic studies (OCT, fundus photos, visual fields), triggering containerized AI inference services, and writing structured results back as DICOM SR or HL7 ORU messages. These results can then overlay on the viewer, pre-populate report drafts, or prioritize the worklist based on clinical urgency.

High-value use cases are workflow-specific. For diabetic retinopathy screening, AI can automatically grade fundus images upon ingestion, flagging referable cases (moderate NPDR or worse) for immediate review and routing non-referable cases to a batch queue, turning a manual grading process into a triaged workflow. For glaucoma management, AI models analyzing serial OCT retinal nerve fiber layer (RNFL) maps can quantify progression rates and highlight areas of significant change, presenting a longitudinal analysis directly beside the current study for faster clinical decision-making. For macular edema, AI can segment and quantify fluid volumes on OCT B-scans, providing objective measurements for treatment response tracking that auto-populate into the quantitative section of a structured report.

A production implementation requires careful governance. AI results should be presented as non-interpretive findings—clearly marked as AI-generated suggestions for the clinician to verify. Integration should support a human-in-the-loop review where the radiologist can accept, modify, or reject AI findings, with all actions logged to an audit trail. Rollout typically starts with a pilot in a single clinic or for a single use case (e.g., DR screening), using Sectra's configuration tools to enable the AI overlay for a specific user group before enterprise-wide deployment. This phased approach builds clinician trust and allows for workflow optimization.

For a deeper technical dive on connecting AI models to the broader Sectra platform, see our guide on AI Integration for Sectra PACS. To understand how to architect these cloud-based inference pipelines, review our blueprint for AI Integration for Cloud-Based PACS AI.

The integration connects at three primary layers within Sectra Ophthalmology: the DICOM/HL7 ingestion pipeline for incoming studies, the structured reporting module for AI-assisted documentation, and the clinical viewer for AI result overlay. For diabetic retinopathy screening, AI models process incoming color fundus photos via a secure DICOMweb listener. Positive findings trigger an automated HL7 ORU message to flag the study in the worklist and pre-populate the Sectra Reporting template with severity grading (e.g., ICDR levels) and recommended follow-up intervals, saving the ophthalmologist from manual grading and data entry.

For quantitative analysis—such as macular edema thickness from OCT or glaucoma progression from serial visual fields—the architecture uses a containerized inference service deployed within the hospital's secure network. The service pulls anonymized series from the Sectra VNA via its RESTful API, processes them, and returns structured measurements (e.g., central subfield thickness in µm) as DICOM Structured Reports (SR). These SR objects are stored back in the VNA and linked to the original study, making the quantitative data available within the same viewer session without disrupting the clinician's workflow. A key governance element is a human-in-the-loop approval step configured in Sectra's workflow manager, ensuring all AI-generated quantitative data is verified and signed off by the reading physician before finalization.

Rollout follows a phased, use-case-specific approach, starting with non-diagnostic automation like automated image quality control for OCT scans, which reduces technologist recall rates. Subsequent phases introduce diagnostic support tools, each requiring clinical validation, user training, and updates to the Sectra role-based access control (RBAC) system to govern which users can see AI prompts versus final results. The entire pipeline is auditable, with all AI inferences, user interactions, and report modifications logged to Sectra's audit trail for compliance and model performance monitoring. For health systems operating at scale, this architecture supports deployment across a hybrid cloud, with inference services hosted on-premise for PHI-bound data and model training/retraining pipelines operating in a secured cloud environment, synchronized via the Sectra Enterprise Imaging platform's orchestration layer.

A production integration for Sectra Ophthalmology must be architected with clinical governance in mind. This typically involves a multi-layered security model where AI inference runs in a secure, HIPAA-compliant container or cloud service, accessing studies via DICOMweb from the Sectra PACS or VNA. All AI-generated observations—such as a DR severity score or macular thickness measurement—are written back as DICOM Structured Reports (SR) or HL7 FHIR observations, creating a permanent, auditable link to the source images within the patient record. Role-based access controls (RBAC) within Sectra ensure only authorized ophthalmologists or technicians can view or act upon AI findings.

We recommend a three-phase rollout to de-risk adoption and build clinical confidence:

1. Silent Validation Phase: AI processes historical OCT and fundus images in the background. Results are stored but not displayed in the clinical viewer. This phase builds a performance baseline and validates the AI against your site's patient population and imaging protocols.
2. Assistive Notification Phase: AI findings are presented as non-interruptive notifications or secondary findings panels within the Sectra viewer. The ophthalmologist's primary workflow is unchanged, but they can optionally review AI-suggested quantitative data (e.g., edema volume, RNFL thickness maps) to inform their diagnosis.
3. Integrated Workflow Phase: AI is fully embedded into key workflows. For example, AI can automatically pre-populate fields in a diabetic retinopathy screening report template or flag studies meeting referral criteria for glaucoma progression, directly within the Sectra worklist. All phases maintain a human-in-the-loop; the AI supports, but does not replace, the clinician's final interpretation.

Change management is critical. Successful implementations establish a clear AI Governance Committee—including lead ophthalmologists, IT, compliance, and clinical engineering—to oversee model validation, monitor for performance drift, and approve progression between rollout phases. This committee also defines protocols for handling AI discrepancies and ensures continuous feedback loops are in place, where clinician corrections can be used to refine local AI performance, all while maintaining strict audit trails for compliance and accreditation.