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The Oyster ACIF Cage is indicated for intervertebral body fusion of the spine in skeletally mature patients. The Oyster ACIF Cage is intended for use for anterior cervical interbody fusion in patients with cervical disc disease (DDD) at up to two contiguous levels from C2 to T1. The System is intended to be used with supplemental fixation; the Oyster ACIF Cage device is required to be used with an anterior cervical plate as the form of supplemental fixation. The System is intended for use with autogenous and/or allogeneic bone graft comprised of cancellous and/or corticocancellous bone graft to facilitate fusion. The cervical devices are to be used in patients who have had at least six weeks of non-operative treatment.

The Oyster cages are Cervical Interbody Fusion cages and have been developed for at up to two contiguous levels from C2 to T1. It is intended for insertion between two adjacent cervical vertebrae. The implants are offered in heights from 4 to 10mm, and 3 footprints (14mm x 15mm, 16mmx17mm, 14mmx17mm). The implants are manufactured by SLM and standard milling process.

This document is a 510(k) summary for the Oyster ACIF Cage, an intervertebral body fusion device. It primarily focuses on demonstrating substantial equivalence to predicate devices through material and mechanical testing, rather than an AI/ML-driven device that would involve a test set, ground truth, and human reader performance studies.

Therefore, many of the requested criteria for an AI/ML device, such as acceptance criteria based on metrics like sensitivity/specificity, sample size for AI test sets, expert adjudication methods, MRMC studies, and training set information, are not applicable to this submission.

Here's an analysis based on the provided document:

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

The document does not provide a table of acceptance criteria in the traditional sense for an AI/ML device (e.g., performance metrics like sensitivity, specificity, AUC). Instead, it focuses on mechanical performance data comparing the device's strength to predicate devices. The acceptance criteria are implicitly that the mechanical test results are "sufficient for its intended use and is substantially equivalent to legally marketed predicate devices."

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

This information is not applicable as the device is a spinal implant, not an AI/ML diagnostic tool. The "testing" refers to mechanical and material property tests on the device itself, not a clinical test set of 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)

This is not applicable. Ground truth, in the context of an AI/ML device, refers to verified labels for medical data, typically established by clinical experts. For a spinal implant, "ground truth" relates to the physical and biological performance of the material and design, established through engineering standards and biological compatibility assessments.

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

This is not applicable. Adjudication methods are used in clinical studies involving interpretation of medical images or data by human experts for ground truth establishment.

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

This is not applicable. MRMC studies are used to evaluate the impact of AI on human reader performance in diagnostic tasks. This device is a physical implant.

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

This is not applicable. Standalone performance is a concept for AI algorithms.

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

For this device, the "ground truth" to which its performance is compared is based on established engineering standards (ASTM F2077, ASTM F2267) and the performance characteristics of legally marketed predicate devices. It's a "truth" derived from physical and mechanical testing, not clinical diagnosis.

8. The sample size for the training set

This is not applicable. This refers to AI/ML model training, which is not relevant for this medical device.

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

This is not applicable.

In summary: The provided document is a 510(k) summary for a physical medical device (spinal implant). The acceptance criteria and performance data discussed are related to the device's mechanical and material properties, rather than its performance as an AI/ML diagnostic tool. Therefore, many of the questions asked, which are specific to AI/ML device evaluation, are not relevant to this submission.

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