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The Inertia® Pedicle Screw and Deformity Correxxion™ System is intended for pedicle and non-pedicle immobilization and stabilization of the posterior non-cervical spine (T1-S2/Ilium) in skeletally mature patients as an adjunct to fusion for the following indications: degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma (i.e., fracture or dislocation), spinal stenosis, cyphosis, kyphosis, or lordosis), tumor, pseudarthrosis, and/or failed previous fusion.

When used for posterior non-cervical pediatic patients, the Inertia® Pedicle Screw and Deformity Correxxion™ System implants are indicated as an adjunct to treat adolescent idiopathic scoliosis. The Inettia® Pedicle Screw and Deformity Correxxion™ System is to be used with autograft. Pediatric pedicle screw fixation is limited to a posterior approach.

The Inertia® Pedicle Screw and Deformity Correxxion™ System consists of longitudinal members (rods), anchors (screws and hooks), interconnections (offset, rod-rod and crosslink) and fasteners in a variety of sizes to accommodate differing anatomic requirements. The Inertia® Pedicle Screw and Deformity Correxxion™ System implants are sold sterile and non-sterile.

The provided text describes a 510(k) premarket notification for the "Inertia® Pedicle Screw and Deformity Correxxion™ System," a medical device for spinal stabilization and immobilization. However, the document does not contain information regarding an AI/Machine Learning-enabled device or a study proving its performance against specific acceptance criteria for such a device.

The acceptance criteria described in the document, and the study conducted, pertain to the mechanical performance of the pedicle screw system itself, not to the performance of an AI algorithm.

Therefore, I cannot provide the details requested in your prompt regarding AI/ML device performance, such as:

• A table of acceptance criteria and reported device performance for an AI/ML device.
• Sample size for the test set and data provenance for an AI/ML device.
• Number of experts and their qualifications for establishing ground truth for an AI/ML device.
• Adjudication method for an AI/ML device.
• MRMC comparative effectiveness study results or effect size for AI assistance.
• Standalone performance for an AI/ML device.
• Type of ground truth used for an AI/ML device.
• Sample size for the training set for an AI/ML device.
• How ground truth for the training set was established for an AI/ML device.

What the document does describe (related to mechanical device performance):

The document implicitly defines acceptance criteria by stating that the device's performance needs to be "substantially equivalent" to predicate devices.

1. A table of acceptance criteria and the reported device performance (for the mechanical device):

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

• Sample Size: Not explicitly stated as a number of devices, but refers to "worst case Inertia® Pedicle Screw and Deformity Correxxion™ System constructs."
• Data Provenance: The tests are described as mechanical testing, implying in-vitro lab testing rather than human patient data.

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

• This is not applicable as the "ground truth" here refers to established engineering standards (ASTM F1717) for mechanical performance, not expert clinical interpretation.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• This is not applicable, as it refers to a clinical or AI-based assessment, not 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 type of study was not done, as the document concerns a mechanical spinal implant, not an AI-assisted diagnostic or therapeutic tool.

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

• Not applicable; this is not an AI device.

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

• The "ground truth" for the mechanical device's performance is established by ASTM F1717 standards for static and dynamic compression bending and static torsion.

8. The sample size for the training set:

• Not applicable; mechanical testing doesn't involve a "training set" in the context of AI/ML.

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

• Not applicable.

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