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CUVIS-spine is intended for use as an aid for precisely locating anatomical structures and for the spatial positioning and orientation of guide bush to be used by surgeons for navigating compatible surgical instruments in open or percutaneous surgical procedures provided that the required markers and rigid patient anatomy can be identified on O-arm or C-arm.

The CUVIS-spine is a mobile system mainly comprising the robotic arm, the main console and the staff console as an option. The robotic arm is positioned on the floor near the side of the surgical table. The location of the main console or the staff console is appropriately determined considering the user preference and the environments. CUVIS-spine is a pedicle screw guide system which consists of Robotic Arm, Main Console, Staff Console, Guide bush, Source Calibrator, Registration Tool, Registration Tool Adapter, Robotic Arm Drape, Tool Drape, Detector Drape, Marker Ball, Patient Marker, Marker Driver, Detector Calibrator, Dilator, Serration-tip Dilator, Drill Bit, Tapper, Stylet Tapper, Screwdriver, Instrument Container.

Here's a breakdown of the acceptance criteria and study information for the CUVIS-spine device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document focuses on claiming substantial equivalence to a predicate device (CUVIS-spine K201569) rather than explicitly stating new, quantifiable acceptance criteria for performance metrics. However, it does mention that "Pose accuracy and Repeatability of the CUVIS-spine were tested and validated." Without specific numerical targets for these, a direct table of acceptance criteria vs. performance is not fully possible.

However, based on the non-clinical tests mentioned, we can infer performance was evaluated against general medical device safety and efficacy standards. The "Accuracy" row in the comparison table with the predicate device also indicates that the proposed device maintains accuracy.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document mentions a cadaveric study was performed for the robotic-assisted pedicle screw placement. However, the sample size (number of cadavers or placement procedures) is not specified in the provided text.
• Data Provenance: The document does not explicitly state the country of origin for the cadaveric study data. It is a non-clinical, prospective study (as it was performed specifically for testing the proposed device).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the document. For a cadaveric study of pedicle screw placement, ground truth would typically be established by imaging (e.g., post-procedure CT scans) reviewed by qualified medical professionals (e.g., orthopedic surgeons or radiologists). The number and qualifications of such experts are not detailed here.

4. Adjudication Method for the Test Set

This information is not provided in the document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, an MRMC comparative effectiveness study involving human readers with and without AI assistance is not mentioned in the document. The study described is a cadaveric study focused on physical performance and accuracy, not reader performance.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

The device itself (CUVIS-spine) is a robotic-assisted surgical system, implying human interaction. The non-clinical tests focused on the device's "Pose accuracy and Repeatability," which are standalone measures of the robot's mechanical performance. However, there isn't a specific mention of an "algorithm only" study in the context of image interpretation or diagnosis separate from the physical robotic guidance. The cadaveric study inherently involves the algorithm driving the robot.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

For the cadaveric study on robotic-assisted pedicle screw placement, an implied ground truth would likely be established through post-procedure imaging (e.g., CT scans) to assess the accuracy of screw placement relative to the planned trajectory and anatomical landmarks. This assessment would typically be performed by experts, but the specifics are not detailed.

8. The Sample Size for the Training Set

This device is a robotic surgical system, not an AI model that learns from large datasets in the traditional sense (e.g., for image classification). Therefore, a "training set" as understood in machine learning for diagnostic AI is not applicable in the context described in this document. The robotic system's "training" would involve mechanical calibration, software development, and validation against engineering specifications, not data-driven learning from patient cases in the same way.

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

As explained in point 8, a "training set" for an AI model learning from data is not described. The ground truth for the robotic system's development would be based on engineering specifications, physical measurements, and validation against known anatomical models and surgical planning parameters.

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