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The S4 Spinal System is intended for anterior/anterolateral and posterior, non-cervical pedicle and non-pedicle fixation. Fixation is limited to skeletally mature patients and is intended to be used as an adjunct to fusion using autograft or allograft. The device is indicated for treatment of the following acute and chronic instabilities or deformities:

1 ) degenerative disc disease (defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies)

2. spondylolisthesis,

3. trauma (i.e., fracture or dislocation),

4. spinal stenosis,

5. deformities or curvatures (i.e., scoliosis, kyphosis, and/or lordosis),

6. tumor,

7. pseudoarthrosis, and

8. failed previous fusion.

The S4 Spinal System consists of polyaxial screws and monoaxial screws of varying diameters and lengths, various hook styles, rods of varying lengths, fixed and adjustable cross-connectors, and various styles of rod connectors. All implant components are top loading and top tightening. The S4 Spinal System is manufactured from Titanium and Titanium alloy in accordance with ISO 5832/3 and ISO 5832/2.

The provided text describes a 510(k) premarket notification for the Aesculap® Implant Systems S4 Spinal System, specifically focusing on a line extension to include additional rod connectors. The evaluation largely relies on demonstrating substantial equivalence to predicate devices through non-clinical performance testing.

Here's an analysis of the acceptance criteria and study described, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: The document does not specify a "sample size" in the context of clinical test subjects or datasets as would be common for AI/diagnostic device studies. Instead, it refers to non-clinical testing of the physical device components. The sample size for these non-clinical tests (e.g., number of rod connectors tested) is not provided.
• Data Provenance: Not applicable in the context of clinical data provenance. The "data" here comes from laboratory mechanical testing of physical devices.

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

• Not applicable. This submission is for a physical spinal implant system, not a diagnostic or AI-driven device requiring expert consensus for ground truth establishment. The "truth" in this context is determined by adherence to ASTM standards and mechanical performance metrics compared to predicate devices.

4. Adjudication Method for the Test Set:

• Not applicable. There is no "test set" in the sense of clinical cases requiring expert adjudication. The performance is assessed through standardized mechanical testing methods per ASTM F1717-11a.

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

• Not applicable. This is a spinal implant, not a diagnostic imaging or AI-assisted interpretation device that would involve human readers.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

• Not applicable. This submission describes a physical medical device, not an algorithm or software.

7. Type of Ground Truth Used:

• For the performance studies, the "ground truth" implicitly refers to the specified performance criteria set by ASTM standards (F1717-11a) for static axial compression bending, dynamic axial compression bending, and static torsion, as well as the performance profiles of legally marketed predicate devices. The device's performance is compared against these benchmarks rather than a clinical ground truth like pathology or outcome data.

8. Sample Size for the Training Set:

• Not applicable. This device is not an AI/machine learning model, so there is no training set in the conventional sense.

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

• Not applicable. As there is no training set, this question is not relevant.

In summary: The provided document describes the regulatory pathway for a spinal implant system, which primarily involves demonstrating mechanical equivalence to predicate devices through standardized non-clinical testing. It does not involve AI, diagnostic algorithms, or human-in-the-loop performance studies.

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