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The DEXA-C Cervical Interbody System is indicated for anterior cervical interbody fusion procedures in skeletally mature patients with degenerative disc disease (DDD) of the cervical spine with accompanying radicular symptoms at one or two contiguous levels from C2-T1. DDD is defined as discogenic pain with the degeneration of the disc confirmed by history and radiographic studies. The device system is designed for use with supplemental fixation and autograff and/or allogenic bone graft composed of cancellous, cortical, and/or cortico-cancellous bone to facilitate fusion and is to be implanted via an open, anterior approach.

The DEXA-C Cervical Interbody System is a porous 3D-printed intervertebral body fusion device that incorporates low-, mid-, or high-density lattice pattern options. The profile of the device is rectangular with a hollow core for bone graft to promote bone integration and fusion between the endplates. The device is available in various footbrints and heights to accommodate variability among patients and is manufactured from titanium alloy per ASTM F3001.

This document is a marketing clearance (510(k) summary) for a medical device called the DEXA-C Cervical Interbody System. It does not describe a study involving an AI/software device that requires intricate acceptance criteria and a detailed study proving the device meets those criteria, as typically seen with diagnostic or prognostic AI systems.

Instead, this document focuses on demonstrating substantial equivalence to existing, legally marketed predicate devices. The "performance data" section refers to non-clinical mechanical testing of the physical interbody fusion device, not a software algorithm.

Therefore, many of the requested points are not applicable to this particular document. I will address the points that are relevant based on the provided text, and explicitly state when a point is not applicable.

Here's an analysis based on the provided document:

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

Not applicable in the context of an AI/software device's performance metrics (e.g., sensitivity, specificity, AUC). For this physical device, the "acceptance criteria" are implied by the mechanical testing standards, and the "performance" is that the device met these standards demonstrating sufficient strength.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

This refers to mechanical testing of the physical device, not a human or image-based test set. The document does not specify the number of devices tested for each mechanical test mode, nor the provenance of "data" in the sense of patient data.

• Sample Size: Not explicitly stated for each mechanical test.
• Data Provenance: Not applicable in the context of clinical or image data. The tests are lab-based mechanical tests.

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. "Ground truth" in this context refers to meeting mechanical engineering standards, not medical diagnoses established by experts.

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

Not applicable. There's no human adjudication process described 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

Not applicable. This is a physical interbody fusion device, not an AI-assisted diagnostic tool.

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

Not applicable. This is a physical interbody fusion device, not a standalone algorithm.

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

Not applicable in the typical AI/software sense. For this device, the "ground truth" for proving performance would be established by the adherence to industry mechanical testing standards (ASTM F2077, ASTM F2267) and demonstrating the mechanical strength and integrity required for its intended use, as compared to predicate devices.

8. The sample size for the training set

Not applicable. This is a physical device, not an AI model that requires a training set.

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

Not applicable.

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