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The ExcelsiusHub™ is intended for use as an aid for precisely locating anatomical structures to be used by surgeons for navigating compatible surgical instruments in open or percutaneous procedures provided that the required fiducial markers and rigid patient anatomy can be identified on CT scans or fluoroscopy. The system is indicated for the placement of spinal and orthopedic bone screws and interbody fusion devices.

The ExcelsiusHub™ is a navigation system that includes hardware and software that enables real time surgical visualization using radiological patient images (preoperative CT, intraoperative CT and fluoroscopy), a dynamic reference base, and a camera tracking system. The navigation system determines the registration or mapping between the virtual patient (points on the patient images) and the physical patient (corresponding points on the patient's anatomy). Once this reqistration is created, the software displays the relative position of a tracked instrument on the patient images. This visualization can help guide the surgeon's planning and approach for implant placement. The patient's scan coupled with the reqistration provides visual assistance to the surgeon when using the system for free hand navigation. During surgery, the system tracks the position of compatible instruments in or on the patient anatomy and continuously updates the instrument position on patient images utilizing optical tracking. System software is responsible for all navigation functions, data storage, network connectivity, user management, case management, and safety functions. ExcelsiusHub™ uses the same instruments as ExcelsiusGPS®.

Here's a breakdown of the acceptance criteria and study information for the ExcelsiusHub™ device, based on the provided text:

Executive Summary: The provided 510(k) summary does not include detailed quantitative acceptance criteria or a specific study that directly proves the device meets those criteria for clinical accuracy. Instead, it mentions "Verification and validation testing" and "Surgical simulations conducted on phantom models" without providing metrics or results. The focus is on demonstrating substantial equivalence to predicates through similar technological characteristics and general performance testing.

1. Table of Acceptance Criteria and Reported Device Performance

Critique: The provided document does not include specific quantitative acceptance criteria (e.g., "accuracy must be within X mm") or a table showing the device's reported performance against such criteria. It states that "Verification and validation testing were conducted... to confirm that the device meets performance requirements." However, the actual performance requirements (acceptance criteria) and the results against them are not detailed.

Therefore, this section cannot be fully constructed from the provided text.

2. Sample Size for the Test Set and Data Provenance

Critique: The document states that "Surgical simulations conducted on phantom models" were performed. However, it does not specify:

• The sample size (number of phantom models or surgical procedures simulated).
• The data provenance (e.g., country of origin, retrospective or prospective). Phantom studies typically don't have a "country of origin" in the same way clinical data does, but the specifics of the phantom models and the simulation environment (e.g., in-house testing facility) are missing.
• Whether human data was used for a test set. The mention of "phantom models" strongly suggests laboratory testing rather than human clinical trials for direct performance evaluation.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

Critique: Since the document mentions "Surgical simulations conducted on phantom models" and not human studies for the primary performance evaluation, the concept of "experts used to establish ground truth" in the clinical sense (e.g., radiologists assessing images) is not directly applicable or described. If the phantom studies involved expert assessment of outcomes, this is not detailed.

4. Adjudication Method for the Test Set

Critique: No information is provided regarding an adjudication method. This would typically be relevant for studies involving human assessment or complex clinical endpoints, which are not described here for performance testing.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Critique: The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. Therefore, there is no information on the effect size of human readers improving with AI vs. without AI assistance. The device is a navigation system, not an AI-assisted diagnostic tool that would typically involve MRMC studies to evaluate reader performance.

6. Standalone Performance (Algorithm Only)

Critique: The document describes the ExcelsiusHub™ as a "navigation system that includes hardware and software that enables real time surgical visualization... and a camera tracking system." Its function is to display the relative position of a tracked instrument on patient images to "help guide the surgeon's planning and approach."

While the software performs "all navigation functions, data storage, network connectivity, user management, case management, and safety functions," the primary performance is as a real-time guidance system with surgeon interaction. A "standalone" performance in the sense of an algorithm making a decision without human-in-the-loop is not the design or intent of this type of device. Its "performance" is inherently linked to its ability to accurately track and display information for the human surgeon, and the testing described ("surgical simulations on phantom models") implicitly involves this human-in-the-loop context. No purely "algorithm only" performance metrics are provided.

7. Type of Ground Truth Used (for Test Set)

Critique: For the "surgical simulations conducted on phantom models," the type of ground truth would typically be:

• Physical measurements: Highly accurate measurements of planned trajectories vs. actual instrument placement in the phantom using a precise measurement system (e.g., coordinate measuring machine, high-resolution CT scans after simulated instrument insertion).
• Design specifications: The known geometric properties and landmarks of the phantom model.

However, the specific methodology for establishing this ground truth (e.g., who performed the measurements, the precision of the measurement tools) is not detailed in the provided text.

8. Sample Size for the Training Set

Critique: The document describes "Software validation and verification testing" but does not mention a "training set" in the context of machine learning or AI models. The device is a navigation system, not explicitly an AI-driven diagnostic or predictive model where a distinct training set for an algorithm would be common. The software lifecycle processes (IEC 62304) and usability engineering (IEC 62366) indicate standard software development and testing, not necessarily an AI training paradigm.

9. How the Ground Truth for the Training Set Was Established

Critique: As no "training set" is mentioned or implied by the device's description as a navigation system, this question is not applicable based on the provided information.

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