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The NuVasive AP Expandable XLIF System is indicated for intervertebral body fusion of the spine in skeletally mature patients. The system is designed for use with autogenous bone graft to facilitate fusion.

The AP Expandable XLIF System is intended for use at either one level or two contiguous levels in the lumbar spine, from L2 to S1, for the treatment of degenerative disc disease (DDD) with up to Grade I spondylolisthesis. DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. Patients must have undergone a regimen of at least six (6) months of non-operative treatment prior to being treated with the AP Expandable XLIF System. The AP Expandable XLIF System is intended to be used with supplemental internal spinal fixation systems (e.g., pedicle screw/rod systems) that are cleared by the FDA for use in the lumbar spine.

The NuVasive AP Expandable XLIF System is manufactured from Ti-6AI-4V ELI conforming to ASTM F136 and ISO 5832-3 and Ti-6Al-4V conforming to ASTM 1472. The implants are available in a variety of different shapes and sizes to suit the individual pathology and anatomical conditions of the patient.

This document is a 510(k) Summary for the NuVasive® AP Expandable XLIF System, an intervertebral body fusion device. The purpose of this summary is to demonstrate that the device is substantially equivalent to legally marketed predicate devices, not to establish performance criteria through a clinical study involving AI or human readers.

Therefore, the requested information components related to acceptance criteria, ground truth, sample sizes for test and training sets, expert adjudication, and multi-reader multi-case (MRMC) studies are not applicable to the content provided in this 510(k) summary. These components are typically associated with performance evaluations of AI/ML-driven devices or clinical trials, neither of which are described here.

The document focuses on demonstrating substantial equivalence through nonclinical testing and comparison to predicate devices, as is standard for many Class II medical devices seeking 510(k) clearance.

Here's the relevant information based on the provided text, addressing the request by noting the inapplicability of many points:

1. Table of Acceptance Criteria and Reported Device Performance

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

Not applicable. No "test set" in the context of AI/ML or clinical data performance evaluation is described. The performance data refers to nonclinical (mechanical) testing of the physical device.

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

Not applicable. No "ground truth" derived from expert review of data is established for this type of nonclinical testing.

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

Not applicable. No "adjudication method" is described as there is no human review of data to establish ground truth.

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. This device is a physical implant, not an AI-driven diagnostic or assistive system. No MRMC study was performed or is relevant.

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

Not applicable. This device is a physical implant, not an algorithm.

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

Not applicable. The "ground truth" for the performance data in this context is based on engineering specifications and physical measurements compliant with ASTM standards, not clinical ground truth like pathology or expert consensus. The comparison is against predicate devices and their established performance characteristics.

8. The sample size for the training set

Not applicable. There is no AI/ML model, and therefore no "training set."

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

Not applicable. There is no AI/ML model, and therefore no "training set" or corresponding ground truth establishment process.

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