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The Rampart™ D Lumbar Interbody Fusion Device is indicated for intervertebral body fusion at one level or two contiguous levels in the lumbar spine from L2 to L5 in patients with degenerative disc disease (DDD) with up to Grade I spondylolisthesis at the involved level(s). DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies. These patients should be skeletally mature and have had six months of non-operative treatment. The Rampart D device is designed for use with autograft and/or allograft comprised of cancellous and/or corticocancellous bone graft as an adjunct to fusion and is intended for use with supplemental fixation systems cleared by the FDA for use in the lumbar spine.

The Rampart™D Lumbar Interbody Fusion Device is an intervertebral implant designed to provide mechanical support within the intradiscal space as an adjunct to fusion. The device is made of PEEK-OPTIMA® LT-1, titanium alloy, polyethylene terephthalate (PET), and tantalum markers. It is available in varying lengths and heights with two lordotic configurations, and is provided sterile. It is designed with a porous central cavity for graft containment. The device features a rounded nose to aid implant insertion and includes ridged teeth to resist migration.

This document is a 510(k) premarket notification for a medical device called the "Rampart™ D Lumbar Interbody Fusion Device." It is not an AI/ML device, and therefore the information requested about acceptance criteria and study proving adherence to criteria in the context of AI/ML are not applicable here.

This document describes a traditional medical device (an interbody fusion device) and demonstrates its "substantial equivalence" to legally marketed predicate devices, as required for 510(k) clearance by the FDA. The "testing" section refers to non-clinical (bench) testing and patient-level clinical data supplemented by a literature review to support this substantial equivalence.

Therefore, the requested information which pertains to AI/ML device evaluation criteria, such as "number of experts used to establish ground truth," "adjudication method for the test set," "MRMC comparative effectiveness study," "standalone performance," "sample size for training set," and "how ground truth for training set was established," are not relevant to this type of device submission and are not found in the provided text.

The "acceptance criteria" for this device are meeting the performance standards of the predicate devices through the non-clinical and clinical testing mentioned, demonstrating it is "substantially equivalent" in safety and effectiveness.

Here's a breakdown of the relevant information from the document:

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

Since this is a non-AI/ML medical device, the "acceptance criteria" are not based on metrics like sensitivity, specificity, or AUC, but rather on demonstrating substantial equivalence to a predicate device through various physical and mechanical tests, and clinical data. The document states:

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

• Test Set (Clinical Data): "Patient-level clinical data" was provided, but the specific sample size, country of origin, or whether it was retrospective or prospective, is not detailed in this 510(k) summary. This information would typically be in the full submission, not in this publicly accessible summary.
• Bench Testing: "All testing was conducted on worst case configurations for both sizing and recommended graft fill." No specific numerical sample sizes for non-clinical (bench) tests are provided in this summary.

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):

• This is not applicable to this device type. Ground truth in the context of an AI/ML device refers to human expert annotations. For a traditional medical device, clinical data (patient outcomes, imaging findings relevant to the DDD diagnosis, etc.) forms part of the supporting evidence, but there's no "ground truth" derived from expert consensus in the same way as for an AI/ML diagnostic algorithm.

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

• Not applicable for this traditional medical device.

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 as this is not an AI/ML device assisting human readers.

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

• Not applicable as this is a physical medical implant, not an algorithm.

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

• For the clinical support, it refers to "patient history and radiographic studies" for the diagnosis of Degenerative Disc Disease (DDD) and overall "patient-level clinical data." The "ground truth" for the device's performance would ultimately be patient outcomes related to successful fusion and safety, compared to the predicate device. However, the details are not provided in this summary.

8. The sample size for the training set:

• Not applicable as this is not an AI/ML device that requires a "training set."

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

• Not applicable as this is not an AI/ML device.

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