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When used as a pedicle screw fixation system in skeletally mature patients, the Dynesys Spinal System is intended to provide immobilization and stabilization of spinal segments as an adjunct to fusion in the treatment of the following acute and chronic instabilities or deformities of the thoracic, lumbar, and sacral spine: degenerative spondylolisthesis with objective evidence of neurologic impairment, and failed previous fusion (pseudoarthrosis).

In addition, when used as a pedicle screw fixation system, the Dynesys Spinal System is indicated for use in patients:

• Who are receiving fusions with autogenous graft only; .
• Who are having the device fixed or attached to the lumbar or sacral spine; .
• Who are having the device removed after the development of a solid fusion mass. .

The Dynesys Spinal System is comprised of a variety pedicle screws sizes, tensioning cords and longitudinal spacers that are uniquely fitted for each individual case. The pedicle screws are manufactured from medical grade titanium alloy. The tensioning cords are manufactured from Sulene-PET (polyethylene-terephthalate). The longitudinal spacers are manufactured from Sulene-PCU (polycarbonate-urethane).

The K043565 510(k) submission for the Dynesys® Spinal System describes the device and its intended use, but it does not include acceptance criteria or a study proving that the device meets such criteria in the context of an AI/ML medical device.

This document pertains to a traditional medical device (spinal fixation system) and its substantial equivalence to a predicate device, rather than an AI/ML-driven diagnostic or treatment system. Therefore, the questions related to AI/ML device performance, such as sample sizes for test and training sets, ground truth establishment, expert qualifications, and MRMC studies, are not applicable to this submission.

The relevant information from the provided text focuses on the comparison of technological characteristics for additional screw components to the existing Dynesys Spinal System, establishing substantial equivalence based on materials, design, and function.

Table of Acceptance Criteria and Reported Device Performance:

Answers to Specific Questions (based on the provided text for a non-AI/ML device):

1. A table of acceptance criteria and the reported device performance: As shown above, specific performance metrics or acceptance criteria for an AI/ML component are not present. The "performance" described is the finding of substantial equivalence of additional screw components to the predicate device based on material, design, and function.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. This submission concerns a physical medical device (spinal fixation system), not an AI/ML device requiring a test set of data. The testing mentioned refers to mechanical or physical characteristics of the screw components.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience): Not applicable. No "ground truth" in the context of an AI/ML diagnostic or predictive model is established in this submission. The evaluation is based on engineering and design principles, and comparison to a predicate device.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. There is no test set in the AI/ML sense to be adjudicated.

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: Not applicable. No AI component is described, so no MRMC study with AI assistance would have been conducted or reported here.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. There is no algorithm mentioned in this submission.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable. The "ground truth" for this device's substantial equivalence is the existing, legally marketed predicate device, and the engineering principles and standards applied to the new components.

8. The sample size for the training set: Not applicable. There is no AI/ML model that would require a training set.

9. How the ground truth for the training set was established: Not applicable. No training set is associated with this device submission.

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