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The Signature Orthopaedics Brisbane ALIF and Gladstone ALIF systems are indicated for use with autogenous bone graft in patients with degenerative disc disease (DDD) at one or two contiguous levels from L2 to S1. These DDD patients may also have up to Grade I spondylolisthesis or retrolisthesis at the involved levels. DDD is defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies. These patients should be skeletally mature and have had six months of nonoperative treatment. Patients with previous non-fusion spinal surgery at the treated level may be treated. These implants may be implanted via a laparoscopic or an open anterior approach. The Brisbane and Gladstone ALIF systems may be used as stand-alone devices or in conjunction with supplemental fixation. When used as a stand-alone device the subject devices must be used with all three screws.

The Signature Orthopaedics Brisbane ALIF and Gladstone ALIF cages are manufactured from PEEK-OPTIMA LT1 per ASTM-F2026. The Screws for Brisbane and Gladstone ALIF cages are manufactured from Ti6A14V alloy per ISO 5832-3 and ASTM-F136.

The Brisbane and Gladstone ALIF cages consist of a wedge-shaped geometry and intended for implantation by an anterior approach. The cages are hollow to allow loading of bone graft. The cages are wedge shaped to restore lordosis of the fused vertebral bodies. The superior and inferior surfaces have serrated teeth to resist expulsion. The cages have three holes each on their anterior faces to facilitate the use of titanium bone screws. The use of the bone screws make the subject cages stand-alone cages. The superior and inferior surfaces of Brisbane ALIF cage is Titanium Plasma Spray (TPS) coated per ASTM-F1580.

This document is a 510(k) premarket notification from the FDA for medical devices, specifically artificial intervertebral fusion devices (Brisbane ALIF Device, Gladstone ALIF Device). The information provided is for a physical medical device (an implantable cage for spinal fusion), not an AI/ML-based medical device.

Therefore, the requested information about "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of AI/ML performance (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance) is not applicable to this document.

The document discusses the mechanical and material performance testing of the device, which are the relevant acceptance criteria for this type of physical implant.

Here's a breakdown of the relevant information from the document regarding acceptance criteria and performance studies, adapted to the context of a physical device:

Acceptance Criteria and Device Performance for a Physical Implantable Device

Since this is a physical medical device (an intervertebral fusion device), the "acceptance criteria" and "study that proves the device meets the acceptance criteria" refer to its mechanical, material, and functional performance, not diagnostic accuracy or AI algorithm performance.

Here's how the information in the document aligns with your request, reinterpreted for a physical device:

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

The document lists the types of non-clinical tests performed, implying that the device met the acceptance criteria for these tests as defined by the ASTM standards. Specific numerical results or pass/fail thresholds are not detailed in this summary.

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

• Sample Size: Not explicitly stated for each test. For mechanical testing, this typically refers to the number of devices or components tested according to the respective ASTM standards.
• Data Provenance: The tests were "Non-clinical testing and engineering evaluations" conducted by Signature Orthopaedics Pty Ltd (located in Australia). The data is retrospective in the sense that it was generated prior to submission for regulatory review.

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

• Not Applicable. This concept is relevant for AI/ML diagnostic devices where human experts establish ground truth for image interpretation. For a physical device, the "ground truth" is established by the physical properties and mechanical performance measured against industry standards (ASTM). The experts involved would be engineers and material scientists.

4. Adjudication method for the test set:

• Not Applicable. Adjudication methods like 2+1 or 3+1 are used for establishing consensus in human interpretation of data for AI/ML ground truth. For physical testing, adherence to a standard (e.g., ASTM) and internal quality control procedures guides the interpretation of results.

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. MRMC studies are for evaluating the performance of AI/ML diagnostic tools with human readers. This document describes a physical medical device.

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

• Not Applicable. This refers to AI algorithm performance. The "performance" here is the physical device's mechanical and material properties.

7. The type of ground truth used:

• Engineering Standards and Specifications: The "ground truth" for this physical device's performance is established by recognized engineering and material standards, specifically ASTM (American Society for Testing and Materials) standards. The manufacturer demonstrated that the device adheres to these standards, which are internationally recognized benchmarks for material and mechanical properties relevant to medical implants.

8. The sample size for the training set:

• Not Applicable. There is no "training set" in the context of a physical medical device; this term is used for AI/ML model development.

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

• Not Applicable. As above, no training set for a physical device.

In summary, the provided document is a regulatory submission for a physical medical implant, not an AI/ML software device. Therefore, many of the requested criteria related to AI/ML performance evaluation are not applicable. The document confirms that non-clinical testing (mechanical, material, and coating properties) was conducted according to relevant ASTM standards to demonstrate the device's safety and effectiveness and its substantial equivalence to predicate devices.

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