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The Camber Sacroiliac (SI) Fixation System is intended for sacroiliac joint fusion for conditions including sacroiliac joint disruptions and degenerative sacroiliitis. When the Camber SI Fixation System is implanted, it must be used with a SICONUS SI Joint Fixation System screw implanted across the same sacroiliac joint.

The Camber SI Fixation System is a fusion device consisting primarily of an open architecture 3D generated titanium body to permit bone growth (fusion) throughout the implant. All internal surfaces have a roughened texture. The upper and lower faces have specifically designed surface approximately 0.5 mm thick to provide a trabecular support structure. In addition, the device utilizes a set of two sharpened anchor plates that translate from within the device in slightly angular opposing lateral directions which provide an anchoring system to fixate the implant between ilium and sacrum. The Camber SI Fixation System device has one footprint: 23x26 mm. Implant heights range from 9 to 13mm in 2 mm increments with 8° angulation.

This document is a 510(k) summary for a medical device (Camber Sacroiliac (SI) Fixation System), not a study analyzing AI performance. Therefore, most of the requested information about acceptance criteria, study design, and AI performance metrics is not applicable.

However, I can extract the relevant information regarding the performance testing of the device itself and how it meets acceptance criteria based on predicate devices.

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

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 provided in the given document. The document states "Testing performed indicate that the Camber SI Fixation System is as mechanically sound as the cleared devices," but does not detail the specific sample sizes for mechanical testing or cadaveric/usability testing.

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 information is not applicable as this document describes mechanical and cadaveric testing for a physical implant, not an AI or diagnostic device that requires expert-established ground truth.

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

This information is not applicable for mechanical or cadaveric testing of a physical implant.

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 information is not applicable as this document describes a physical medical device (sacroiliac joint fixation system), not an AI-powered diagnostic or assistive tool for human readers.

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

This information is not applicable as this document describes a physical medical device, not an algorithm.

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

For the mechanical testing, the "ground truth" or benchmark was established by the performance of the predicate devices. For cadaveric and usability testing, the "ground truth" would be related to anatomical feasibility, surgical ease of use, and stabilization, which is typically assessed by surgeons or technical experts during the testing process. The document does not specify the exact methods or criteria for "ground truth" in these non-mechanical tests beyond indicating they were performed to establish substantial equivalence.

8. The sample size for the training set

This information is not applicable as this document describes a physical medical device and its mechanical testing, not a machine learning model that requires a training set.

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

This information is not applicable as this document describes a physical medical device and its mechanical testing, not a machine learning model.

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