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The VIRTU Lumbar Spacer System is indicated for spinal fusion procedures in skeletally mature patients with degenerative disc disease (DDD) at one or two contiguous levels in the lumbar spine (L2-S1) with up to Grade 1 spondylolisthesis or retrolisthessis at the involved level(s). DDD is defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies. Patients with previous non-fusion spinal surgery at the involved level may be treated with the VIRTU Lumbar Spacer System.

Patients must have undergone a regimen of at least six (6) months of non-operative treatment prior to being treated with the VIRTU PLIF/TLIF System.

The VIRTU Lumbar Spacer System is designed for use with autogenous bone graft to facilitate fusion. The system is also intended to be used with supplemental internal spinal fixation systems that are cleared by the FDA for use in the lumbar spine.

The VIRTU Posterior Lumbar Spacer System consists of implants, trials and instruments. The VIRTU Lumbar Spacer System may be implanted bilaterally using a posterior (PLIF) approach, or as a single device employing a transforaminal (TLIF) approach.

This document describes a 510(k) premarket notification for the "VIRTU Lumbar Spacer System", a medical device. As such, it does not present independent clinical studies or acceptance criteria for AI/ML device performance. Instead, it demonstrates substantial equivalence to predicate devices based on non-clinical performance data.

Therefore, many of the requested categories for AI/ML device performance (like sample size for test set, ground truth experts, MRMC studies, standalone performance, training set size, etc.) are not applicable to this submission.

However, I can extract information related to the device's non-clinical performance acceptance criteria and the study that proves it meets them.

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

Based on the document, the acceptance criteria are implicitly tied to demonstrating performance "sufficient for its intended use" and "substantially equivalent to legally marketed predicate devices." While specific numerical acceptance criteria are not explicitly stated in the provided text, the device was tested against recognized ASTM standards. The reported device performance is that it met these standards and was found to be substantially equivalent.

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

The document does not specify the sample size for the mechanical tests. The data provenance is "non-clinical testing" conducted by the submitter (Met 1 Technologies, LLC). This is laboratory testing, not human patient data.

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. This is mechanical testing to established engineering standards, not human expert consensus. The "ground truth" is adherence to the specified ASTM testing methodologies and achieving results comparable to predicate devices.

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

Not applicable. This is mechanical testing, not a clinical trial with human disagreement.

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 spinal implant device, not an AI diagnostic tool.

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

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

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

For the non-clinical performance data, the "ground truth" is adherence to established mechanical testing standards (ASTM standards) and the resultant data demonstrating the device's mechanical properties. The benchmark for "sufficient" is implied to be comparable performance to predicate devices.

8. The sample size for the training set

Not applicable. This is not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

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