AI Integration for Sectra Orthopaedics

AI integration for Sectra Orthopaedics focuses on connecting specialized musculoskeletal algorithms to the core surfaces where surgeons and radiologists review and measure. The primary integration points are the Orthopaedics module's measurement tools, the structured reporting interface, and the PACS worklist. AI models for automated angle measurement (e.g., Cobb, hip-knee-ankle), joint space narrowing quantification, and fracture detection from X-rays and MRIs are triggered as a background service. When a study is loaded, the AI processes the DICOM series, and results are returned as DICOM Structured Reports (SR) or directly as interactive annotations and measurements that populate the surgeon's existing toolset, eliminating manual caliper placement.

The high-value workflow is the pre-operative planning and follow-up review. For a knee osteoarthritis case, the AI can automatically measure medial and lateral joint space width from a weight-bearing X-ray, calculate alignment angles, and compare these metrics to prior exams. These quantitative results are pushed into the structured report template, providing an objective baseline and progression tracking. For trauma, an AI detection algorithm can highlight potential fractures on the initial scout view, prompting a focused review and potentially reducing missed findings in high-volume emergency settings. This integration turns the PACS viewer from a passive display into an active diagnostic assistant.

A production implementation is typically wired through Sectra's Enterprise Imaging SDK and PACS API. A secure, containerized inference service, often hosted on-premise or in a compliant cloud, listens for DICOM study arrival events. After processing, it posts results back to the PACS, where they are linked to the original study. Governance is critical: all AI-generated findings are presented as "suggestions" with confidence scores, requiring surgeon verification and sign-off. An audit trail logs every AI interaction, and the system is rolled out in phases—starting with non-critical measurements in elective planning—to build clinical trust and refine the human-AI workflow. For a deeper dive on integrating with the broader Sectra platform, see our guide on AI Integration for Sectra PACS.

The integration connects AI inference services to the Sectra Orthopaedics PACS module via its RESTful APIs and DICOM Web services. The primary data flow begins when a new X-ray or MRI study (e.g., knee, hip, spine) is stored in the Sectra VNA. A DICOM Study-Completed notification triggers an automated routing job to a secure, HIPAA-compliant inference queue. The AI service—hosted on-premises or in a private cloud—retrieves the anonymized images via WADO-RS, executes the relevant musculoskeletal models (e.g., for joint space narrowing, Cobb angles, fracture detection), and returns structured results as a DICOM Structured Report (SR) or a JSON payload via API. This output is then ingested back into Sectra and attached to the original study as a secondary capture or linked finding object, ready for surgeon review.

Within the Sectra workstation, the AI results are surfaced contextually. For a knee X-ray, the measurement tools panel can auto-populate with suggested tibiofemoral angles and joint space widths. For spine studies, the surgical planning workspace can display automated Cobb angle measurements overlaid on the image. The reporting interface is enhanced, where the AI-generated findings and measurements are presented as draft text or structured data points, allowing the surgeon to verify, adjust, and sign off with a single click. Critical alerts, such as a detected fracture, can trigger an HL7 ADT message to the EHR or a priority flag on the orthopaedic worklist, ensuring rapid surgical team notification.

A phased rollout is recommended, starting with a single, high-volume workflow like post-operative hip X-ray review for implant positioning. Governance is maintained through a human-in-the-loop approval step before any AI measurement is finalized in the report, creating an audit trail. Integration points must be configured within Sectra's IDC7 workflow engine to manage the routing logic and ensure failed AI analyses default to the standard manual workflow without disrupting clinical operations. This architecture ensures AI augments the surgeon's expertise within their native toolset, turning manual measurement tasks from a 5-10 minute process into a near-instant verification step, directly impacting surgical planning efficiency and report consistency.

The integration architecture is designed to operate within the existing Sectra security model. AI inference typically runs in a dedicated, HIPAA-compliant cloud environment or an on-premises GPU cluster. DICOM studies are pulled from the Sectra PACS via secure, encrypted DICOMweb or REST APIs using service accounts with least-privilege access. Results—such as automated measurements for joint space width or fracture probability scores—are returned as DICOM Structured Reports (SR) or JSON payloads, which are then ingested back into the Sectra Orthopaedics module and attached to the original study. This keeps the AI as a stateless service, never storing Protected Health Information (PHI) long-term, and ensures all data access is logged in the PACS audit trail.

A phased rollout is critical for clinical adoption and risk management. We recommend starting with a silent validation phase, where AI runs in the background on a subset of studies (e.g., knee X-rays) and its outputs are compared to radiologist reports without impacting the clinical workflow. This builds a performance baseline. The next phase introduces assistive overlays, where AI-generated measurements and markers are presented as a non-obstructive layer in the Sectra viewer, requiring the surgeon to actively accept or modify each finding. The final phase enables automated draft reporting, where the AI populates structured report templates within the Sectra Orthopaedics reporting interface, significantly reducing manual measurement time while maintaining the surgeon as the final signatory.

Governance is enforced through a human-in-the-loop review mandate for all AI-generated clinical content. Every AI suggestion must be verified, adjusted, or rejected by the credentialed user within Sectra before being finalized. This creates a clear audit trail of human oversight. Furthermore, we implement continuous monitoring to track AI performance metrics (e.g., measurement drift against a gold-standard dataset) and user interaction patterns (acceptance/rejection rates). This operational data feeds into a regular review cadence with clinical and IT leadership, ensuring the AI remains a reliable tool and allowing for model retraining or workflow adjustments as needed. For broader context on embedding AI across an enterprise imaging strategy, see our guide on Enterprise Imaging AI.