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The DSG™ Zavation Screw System is indicated for use with the Zavation Spinal System during pedicle screw insertion to provide feedback to the surgeon via visual and audible alerts that indicate a change in impedance at the tip of the pedicle screw and may indicate contact of the tip with soft tissues and possible vertebral cortex perforation. The DSG™ Zavation Screw System is indicated for use in both open and percutaneous (MIS) surgical approaches to the spine, with options of direct insertion of the screw in bone or after a step of preparation of the pilot hole with sensor equipped instruments.

The DSG™ Zavation Screw System is a modification to the cleared DSG™ Threaded Drill System and consists of the DSG™ Electronic T-handle, Ratcheting Handle, DSG™ Pin (active stylet), and the previously cleared Zavation Spinal System (K153404). These components are purchased and shipped as a complete system from Zavation, with the DSG™ Threaded Drill System components and Zavation Spinal System components individually packaged. The complete system is provided with the modified instructions for use of the DSG™ Zavation Screw System.

All of the patient-contacting materials are categorized per FDA's guidance on ISO 10993-1 as externally communicating materials that are in contact with the body for a limited duration, and are unchanged from the prior clearance. Certain components of the device are single-use while others are re-usable; certain components are provided sterile while others are sterilized by the end user.

The device is intended for use by surgeons in a professional healthcare environment, and utilizes sensing technology to detect the impediately surrounding tissues while inserting pedicle screws either through a previously drilled pilot hole or directly into bone. The surgeon can either drill and/or tap the screw hole prior to inserting the pedicle screw, or can use the system to directly insert the screw into the bone without a pilot hole. As the screw is manually advanced into the bone, the distal sensor measures the electrical impedance of the immediately surrounding tissues. The device produces real-time visual and audible signals to indicate changes in impedance associated with possible vertebral perforation.

The provided text describes the 510(k) summary for the SpineGuard DSG™ Zavation Screw System. It outlines the device, its intended use, and comparative information with a predicate device. However, it does not detail specific acceptance criteria with numerical targets or a comprehensive study plan with the level of detail requested for AI/device performance.

Based on the information provided, here's what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance:

The document describes "performance testing" but does not define explicit acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy, or a specific range of values for mechanical properties). Instead, it states that "All tests were passed, demonstrating equivalent performance according to device specifications and thus supporting substantial equivalence." The tests are:

Missing Information: Specific quantitative acceptance criteria (e.g., "placement accuracy within X mm" for cadaver testing, or specific thresholds for mechanical integrity). The "Pass" result indicates that the device met internal specifications, but these specifications are not detailed in the provided text.

2. Sample size used for the test set and the data provenance:

• Test Set Sample Size: Not specified for any of the performance tests. For "Cadaver Testing," the sample size (number of cadavers, or number of pedicle screws inserted) is not mentioned. For "Mechanical Testing," the number of units tested is also not specified.
• Data Provenance: Not specified. It's unclear if the cadaver testing was performed in the US or another country. The document notes the sponsor is in France.
• Retrospective or Prospective: Not explicitly stated, though cadaver testing would typically be considered prospective for the device evaluation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. For cadaver testing, it would likely involve surgeons, but their number and experience are not mentioned.

4. Adjudication method for the test set:

• Adjudication Method: Not specified. If multiple experts were involved (which is not stated), the method for resolving discrepancies (e.g., 2+1, 3+1, none) is not described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

This device is not an AI/ML-driven diagnostic or image analysis tool for "human readers." It's a surgical guidance system providing real-time feedback (visual and audible alerts) based on impedance measurements during pedicle screw insertion. Therefore, an MRMC study related to human readers improving with AI assistance is not applicable to this device. The "feedback to the surgeon" is a direct function for intraoperative guidance, not a tool for interpreting images or data that human "readers" would then review.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

The device's function is inherently "human-in-the-loop" as it provides feedback to the surgeon. It's not a standalone diagnostic algorithm. The "performance data" describes the device's accuracy and integrity when used by a human. So, a standalone algorithm performance without human involvement is not applicable in the AI sense. Its "standalone" performance would be about the accuracy of its impedance detection, which is implicitly covered by the "Pass" results in the cadaver and mechanical testing.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Cadaver Testing: The "placement accuracy" implies that the true anatomical position and any perforations would be verified (e.g., radiography, CT scan, or direct visual inspection post-dissection), which would serve as the ground truth. However, the specific method for establishing this ground truth is not detailed.
• Mechanical Testing: Ground truth would be based on engineering specifications and measurements (e.g., force, torque, displacement thresholds).

8. The sample size for the training set:

This device does not appear to be an AI/ML device that requires a distinct "training set" in the context of machine learning. Its operation is based on pre-programmed impedance thresholds for tissue differentiation. Therefore, this question is not applicable in the context of the provided information.

9. How the ground truth for the training set was established:

As there's no mention of a "training set" in the context of an AI/ML algorithm, this question is also not applicable. The device's impedance thresholds would likely be established through prior research and experimentation on tissue types, rather than a "training set" with established ground truth labels in the machine learning sense.

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