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The 3D Printed PEEK Interbody System (ALIF) is intended for spinal fusion procedures in the lumbosacral spine at one or two contiguous levels from L2 to S1 in skeletally mature patients with degenerative disc disease (DDD). DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. These patients should have had six months of non-operative treatment prior to treatment with an intervertebral body fusion device. These patients may have had a previous non-fusion spinal surgery at the involved spinal level(s). DDD patients may also have Grade 1 spondylolisthesis or retrolisthesis at the involved levels. The device must be used with supplemental fixation and is intended for use with autograft and/or allogenic bone graft composed of cancellous and/or corticocancellous bone graft. The device is to be implanted via an anterior approach.

The 3D Printed PEEK Interbody System (Cervical) is intended for spinal fusion procedures in the cervical spine at one or two contiguous levels from C2 to T1 in skeletally mature patients with degenerative disc disease (DDD). DDD is defined as neck pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. These patients should have had six weeks of non-operative treatment prior to treatment with an intervertebral body fusion device. These patients may have had a previous non-fusion spinal surgery at the involved spinal level(s). The device must be used with supplemental fixation and is intended for use with autograft and/or allogenic bone graft composed of cancellous and/or corticocancellous bone graft. The device is to be implanted via an anterior approach.

The 3D Printed PEEK Interbody System devices are spinal fusion implants that are inserted into the intervertebral body space of the spine to act as disc spacers and hold bone graft. These devices are manufactured from PEEK OPTIMA™ LT1AMR5 with tantalum radiographic markers and are offered in multiple footprint, height, and lordotic angle options.

This document, a 510(k) Premarket Notification from the FDA, does not describe the acceptance criteria and study proving a software device meets acceptance criteria. Instead, it details the substantial equivalence of a physical medical device, the "3D Printed PEEK Interbody System," to existing legally marketed predicate devices.

Therefore, many of the requested points for describing an AI/software device's acceptance criteria and study (especially points 2, 3, 4, 5, 6, 7, 8, and 9) are not applicable to the information provided in this document. The document focuses on mechanical and physical properties rather than algorithmic performance.

However, I can extract the relevant information regarding the non-clinical testing performed for this physical device and structure it to the best of my ability, highlighting what is present and what is not.

Acceptance Criteria and Study for 3D Printed PEEK Interbody System (Physical Device)

This document describes the non-clinical testing performed to demonstrate the substantial equivalence of the "3D Printed PEEK Interbody System." The acceptance criteria are implicitly defined by meeting or exceeding the performance of the predicate devices according to established ASTM standards and internal lab protocols.

1. Table of Acceptance Criteria and Reported Device Performance:

The document broadly states that the non-clinical testing demonstrates the subject devices "have at least equivalent mechanical strength as the predicates and are as safe, as effective, and perform as well or better than these legally marketed devices." Specific numerical acceptance criteria and reported performance values are not provided in this summary. The tests performed are listed as follows:

Applicability Note: The questions below are typically relevant for AI/software medical devices and are largely not applicable to this 510(k) submission for a physical medical device. Where possible, I will state "Not Applicable" or explain why the information isn't present for a physical device.

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

• Sample Size: Not specified in this 510(k) summary. For mechanical bench testing, sample sizes are typically defined by the relevant ASTM standards or internal validation plans, but these details are not disclosed in the public summary.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable in the context of benchtop mechanical testing for a physical device. The testing is performed in a laboratory setting.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not Applicable. For mechanical testing, "ground truth" is established by the physical properties and performance metrics measured according to established engineering standards (ASTM). No human experts are used to interpret images or diagnose conditions for AI ground truth.

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

• Not Applicable. Adjudication methods are relevant for human interpretation of data, typically in AI studies or clinical trials, not for direct mechanical property testing.

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 physical device, not an AI device designed to assist human readers.

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

• Not Applicable. This question pertains to AI algorithm performance.

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

• Not Applicable directly. For mechanical testing, the "ground truth" refers to the quantitatively measured physical properties and performance values (e.g., strength, stiffness, deformation) obtained through validated test methods (ASTM standards, internal protocols). Comparative performance against predicate devices serves as a benchmark.

8. The sample size for the training set:

• Not Applicable. This device is a physical product, not a machine learning model; therefore, it does not have a "training set."

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

• Not Applicable. As there is no training set for a physical device, no ground truth needed to be established in this context.

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