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The RIGEL™ 3DR Anterior Cervical Corpectomy System is indicated for vertebral body replacement in the cervical spine (C2-T1) in skeletally mature patients. The RIGEL™ 3DR Anterior Cervical Corpectomy System is intended to replace a diseased or damaged vertebral body caused by fracture, or tumor, or for reconstruction following corpectomy performed to achieve decompression of the spinal cord and neural tissues in cervical degenerative disorders. The System is intended to be used with supplemental fixation that has been cleared by the FDA for use in the cervical spine. The System is designed for use with autogenous and/or allogenic bone graft comprised of cancellous and/or corticocancellous bone graft as an adjunct to fusion. The System is also intended to restore the integrity of the spinal column even in the absence of fusion for a limited time period in patients with advanced stage tumors involving the cervical spine in whom life expectance is of insufficient duration to permit achievement of fusion, with bone graft used at the surgeon's discretion.

The RIGEL™ 3DR Anterior Cervical Corpectomy System is a vertebral body replacement device intended to provide structural stability and mechanical support to the cervical spine following corpectomy.

The RIGEL™ 3DR Anterior Cervical Corpectomy System consists of implants additively manufactured from Titanium-6 Aluminum-4 Vanadium ELI per ASTM F3001 and instrumentation manufactured from Stainless Steel per ASTM F899. The RIGEL™ 3DR Anterior Cervical Corpectomy System implants are offered in multiple configurations and different sizes to accommodate various patient anatomical requirements. The implants are provided sterile packed and are intended for single use only.

This document is a 510(k) Premarket Notification from the FDA regarding the RIGEL™ 3DR Anterior Cervical Corpectomy System. It primarily focuses on the substantial equivalence of this medical device to previously cleared predicate devices.

It is crucial to understand that this document describes a medical device (an implant), not an AI/Software as a Medical Device (SaMD). Therefore, many of the requested points related to AI/SaMD performance criteria, expert adjudication, MRMC studies, and ground truth for training sets are not applicable.

Here's an analysis based on the provided text, highlighting the non-applicability of SaMD-specific questions:

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

The document does not explicitly state acceptance criteria in a quantitative table format as one would expect for an AI/SaMD. Instead, it relies on the concept of "substantial equivalence" to predicate devices. The performance data presented are mechanical tests designed to show that the device meets established standards for spinal implants and performs comparably to its predicates.

2. Sample sized 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 document describes mechanical testing of a physical implant, not a software device evaluated on a dataset. The "test set" in this context refers to the physical samples of the implant tested as per ASTM standards. The specific number of physical samples for each test (e.g., how many implants underwent compression testing) is not detailed. Data provenance such as country of origin or retrospective/prospective is not relevant for mechanical device 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 is not applicable. Ground truth, in the context of SaMD, refers to establishing the correct diagnosis or outcome from medical images/data, often by expert consensus. For a physical implant, "ground truth" is established by adherence to engineering specifications and performance against recognized ASTM standards. There are no medical experts establishing ground truth for the performance of the implant itself in this context, although clinical expertise guides the design and intended use.

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

This is not applicable for a physical device. Adjudication methods are used in SaMD studies to resolve discrepancies between expert readers on a test set to establish a definitive ground truth. For mechanical testing, the "adjudication" is inherent in the standardized test procedures and their objective measurements.

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 specifically designed to evaluate the impact of AI on human reader performance. This document concerns a physical device, not an AI.

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

This is not applicable. A standalone performance evaluation refers to an AI algorithm making decisions without human intervention. This document is about a physical spinal implant.

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

As mentioned, the concept of "ground truth" as it applies to SaMD is not applicable here. For this physical device, the "truth" of its performance is derived from standardized mechanical testing (ASTM F2077-18 for static and dynamic compression/torsion, ASTM F2267-04 for subsidence). These tests provide objective, measurable data points against established industry standards for material strength, durability, and biocompatibility rather than a clinical ground truth.

8. The sample size for the training set

This is not applicable. Training sets are used to train AI algorithms. This document describes a physical medical device. While there's a design and development process for the implant, it doesn't involve "training" in the AI sense.

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

This is not applicable for the same reasons as above.

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