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The Zenith Pedicle System is intended to provide immobilization and stabilization of spinal segments in skeletally mature patients 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, trauma (i.e., fracture or dislocation), deformities or curvatures ( i.e., scoliosis, kyphosis, and/or lordosis), spinal tumor, and failed previous fusion (pseudarthrosis).

In addition, this device is a pedicle screw system indicated for the treatment of severe spondylolisthesis (Grade 3 and 4) of the L5-S1 vertebra in skeletally mature patients receiving fusion by autogenous bone graft having implants attached to the lumbar and sacral spine (L3-S1) with removal of implants after the attainment of a solid fusion.

The Apollo Spine Zenith Pedicle Screw System consists of a variety of rods and screws, which can be rigidly locked into a variety of configurations, with each construct being tailor made for the individual case. Multi axial implant screws are supplied in 5.5mm, 6.5mm, 7.5mm and 8.5mm diameter sizes. All sizes are able to receive 5.5 mm connecting rods only. All implant components are fabricated from medical grade titanium alloy (TI-6AI-4V ELI) conforming to ASTM F136 or equivalent.

The Apollo Spine Zenith Pedicle Screw System is a temporary implant system, intended to be removed after solid fusion has occurred. Implant components should not be used with components from any other system or manufacturer. As with all orthopedic implants, components should not be reused.

The provided text describes the Apollo Spine Zenith Pedicle System, a spinal implant system. The regulatory submission primarily focuses on demonstrating substantial equivalence to predicate devices through mechanical testing, rather than an AI/ML-driven device with performance criteria based on clinical or diagnostic accuracy.

Therefore, many of the requested criteria related to AI/ML device performance, such as sample sizes for test sets, data provenance, expert ground truth establishment, adjudication methods, MRMC studies, standalone performance, and training set details, are not applicable to this submission.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated as a separate "test set" for performance evaluation in the context of AI/ML. The "samples" referred to are test articles (medical device components) used in mechanical testing. The dynamic compression testing included "2 runout samples to 5 million cycles," indicating at least two physical samples for that specific test.
• Data Provenance: Not applicable in the AI/ML sense. The data is generated from laboratory mechanical testing of the device components.

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

• Not applicable. This submission is for a physical medical device and relies on engineering standards (ASTM F 1717-04) for performance validation, not expert-derived ground truth for diagnostic accuracy.

4. Adjudication method for the test set

• Not applicable. This relates to concordance among expert readers, which is irrelevant for mechanical testing.

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

• No. This is not an AI-assisted diagnostic device.

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

• Not applicable. This is not an algorithmic device.

7. The type of ground truth used

• The "ground truth" for this device's performance is adherence to the ASTM F 1717-04 standard for spinal implant mechanical testing and the demonstration of substantial equivalence to predicate devices based on the results of these tests and their known performance.

8. The sample size for the training set

• Not applicable. This is not an AI/ML device and does not involve a training set.

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

• Not applicable. This is not an AI/ML device and does not involve a training set.

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