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The AIS SIBD XP Spinal System is a stand-alone device intended to be used with the four supplied bone screws if no supplemental fixation is used.

As an intervertebral body fusion device designed for use with autograft, the SIBD XP Spinal System is intended for spinal fusion procedures at one or two contiguous levels in the lumbar spine from L2 to S1 in patients with degenerative disc disease (DDD) with up to Grade 1 spondylolisthesis at the involved level(s). DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies. These patients may have had previous non-fusion spinal surgery at the involved spinal level(s).

Patients should be skeletally mature and must have undergone a regimen of at least six (6) months of non-operative treatment prior to being treated with the Aesculap® Implant Systems device.

The AIS SIBD XP Spinal System is an implantable spinal device manufactured from PEEK-OPTIMA® LT (Polyetheretherketone) per ASTM F2026, with a titanium laver and a vacuum plasma spray coating (Plasmapore®). The device will have Tantalum markers per ASTM F-560.The implant is secured to vertebral bodies by four titanium screws inserted through the anterior screw holes. The implants are offered in a variety of shapes and sizes to meet the requirements of the individual patient anatomy.

This is a medical device submission, not an AI/ML device, so many of the requested fields are not applicable. Here's what can be extracted:

Acceptance Criteria and Device Performance Study

The submission describes the Aesculap® Implant Systems (AIS) - SIBD XP Spinal System as a substantially equivalent device to its predicate. The "acceptance criteria" are implied by the performance of the predicate devices and existing ASTM standards. The study performed is a series of non-clinical tests to demonstrate that the new device meets or exceeds these established performance benchmarks.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not specified. Standard practice for these types of mechanical tests would typically involve a small number of samples (e.g., 5-10 per test condition) to establish statistical significance within the test's scope.
• Data Provenance: Not explicitly stated, but these are non-clinical tests, meaning they were conducted in a laboratory setting. Not applicable to country of origin or retrospective/prospective in the conventional sense.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Not Applicable. This is for a physical implant device and involves mechanical and material testing, not expert review of diagnostic interpretations. The "ground truth" is defined by established engineering and material science standards (ASTM).

4. Adjudication Method for the Test Set

• Not Applicable. Mechanical tests are evaluated against predefined acceptance limits set by ASTM standards and comparison to predicate devices, not by human adjudication of ambiguous results.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Not Applicable. This is a mechanical device, not an AI diagnostic tool.

6. Standalone (Algorithm Only) Performance

• Not Applicable. This is a mechanical device, not an AI algorithm.

7. Type of Ground Truth Used

• Engineering and Material Standards: The ground truth is established by recognized industry standards, specifically ASTM (American Society for Testing and Materials) standards for spinal implants and coating characteristics. The performance is also benchmarked against legally marketed predicate devices.

8. Sample Size for the Training Set

• Not Applicable. This is a physical device, and the concept of a "training set" is not relevant here. The design of the device is based on established engineering principles and prior validated devices.

9. How the Ground Truth for the Training Set Was Established

• Not Applicable. As there is no training set, this question is not relevant. The device's design is informed by existing knowledge, predicate devices, and regulatory requirements rather than a data-driven training process.

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