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The ETurn spinal implant is indicated for spinal fusion procedures in skeletally mature patients with degenerative disc disease (DDD) in the lumbar spine at one or two contiguous levels from L2 to S1. DDD is defined as discogenic back pain with degeneration of the disc confirmed by patient history and radiographic studies. The DDD patients may also have up to Grade I spondylolisthesis at the involved level. The ETurn devices are to be used with autogenous bone graft. Patients should have had at least six (6) months of non-operative care prior to treatment with this device. The ETurn spinal implant is intended for use with supplemental fixation such as posterior fixation by pedicle screws and rods.

The icotec ETurn Spinal Implant is a hollow, curved frame spinal implant comprised of carbon fiber reinforced polyetheretherketone or CF/PEEK containing 62% ± 3% carbon fibers (by volume) with 50 um tantalum threads, ≤ 0.5% (by volume). It consists of a windowed body with a central slot, a distraction/insertion wedge and surface treads on its cranial and caudal surfaces that serve to guide and anchor the implant. The smooth tread surface helps to create a significantly larger pressure-bearing contact surface between the implant and the bone than the conventional pointed anchoring elements.

The provided text describes a 510(k) summary for a medical device, the icotec ETurn Spinal Implant. It focuses on demonstrating substantial equivalence to predicate devices through functional and safety testing, primarily mechanical performance tests. This document does not describe a study involving an AI/Machine Learning device or its acceptance criteria. Therefore, most of the requested information regarding AI device evaluation cannot be extracted.

However, I can provide what is present about the device's testing:

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

Note: Specific numerical acceptance criteria or performance metrics are not detailed in this summary; rather, the performance is stated as "similar to" or "substantially equivalent" to 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)

• The document describes preclinical testing (Finite Element Analysis and various mechanical tests per ASTM standards). These are typically laboratory-based tests of physical device properties, not clinical studies with patient data.
• Therefore, the concepts of "test set sample size" for patient data, "data provenance," "retrospective or prospective," or "country of origin" are not applicable here.

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 the study described is a preclinical mechanical performance study, not a study requiring expert-established ground truth on patient data.

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

• Not applicable as this is a preclinical mechanical performance study.

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

• No. This is a preclinical mechanical performance study for a spinal implant, not an AI/Machine Learning device, and therefore no MRMC study was conducted or is relevant.

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

• No. This is a physical spinal implant, not an algorithm, so "standalone performance" in this context is not applicable.

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

• N/A. For preclinical mechanical testing, the "ground truth" would be the engineering specifications and performance characteristics of the predicate devices or established mechanical principles, against which the new device's performance is compared.

8. The sample size for the training set

• Not applicable; this is a physical medical device, not an AI/Machine Learning model that requires a training set.

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

• Not applicable; no training set exists for this device.

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