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The PSG 5.5mm Cannulated Pedicle Screw System is intended to provide immobilization and stabilization of spinal segments as an adjunct to fusion in the treatment of the following acute and chronic instabilities or deformities of the thoracic, lumbar, and sacral spine: degenerative disc disease (defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma (i.e., fracture or dislocation), deformity, or curvature (i.e., scoliosis, kyphosis, and lordosis), tumor, stenosis, pseudoarthrosis, and previous failed fusion.

The PSG 5.5mm Cannulated Pedicle Screw System is a non-cervical spinal fixation system. Pedicle screw fixation is limited to skeletally mature patients.

The PSG 5.5mm Cannulated Pedicle Screw System is a multiple component, posterior spinal fixation system which consists of pedicle screws, rods, cross-connectors, and locking cap set screws. All of the components are available in a variety of sizes to match more closely to the patient's anatomy. All components are made from titanium alloy described by such standards as ASTM F136.

The provided document is a 510(k) premarket notification for a medical device called the "PSG 5.5mm Cannulated Pedicle Screw System." This document focuses on demonstrating substantial equivalence to predicate devices based on design, materials, and mechanical performance rather than clinical efficacy studies in the context of AI/software-as-a-medical-device (SaMD). Therefore, many of the requested details about acceptance criteria, clinical study design, and AI performance metrics are not applicable or available in this submission.

However, I can extract the information relevant to the device's mechanical performance and what the submission provides in place of clinical study details.

1. Table of Acceptance Criteria and Reported Device Performance

For medical devices such as pedicle screw systems, "acceptance criteria" and "reported device performance" are typically defined by engineering standards to ensure mechanical integrity and safety. In this context, the device's performance is compared against these standards and to predicate devices.

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

The document does not specify the exact sample sizes (number of devices or test repetitions) used for the mechanical tests. It only states that the device "has been tested" according to the ASTM standards.

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

This is not applicable as the device is a mechanical implant, not an AI/SaMD that requires expert ground truth for image interpretation or diagnosis. The "ground truth" for mechanical testing is adherence to established engineering standards (ASTM F1717-13).

4. Adjudication method for the test set

Not applicable. Mechanical testing is objective and relies on measurements and adherence to specified test protocols, not human adjudication 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. This device is a pedicle screw system, not an AI/SaMD for diagnostic imaging or decision support. No MRMC study was performed.

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

Not applicable. This device is a mechanical implant, not an algorithm.

7. The type of ground truth used

The "ground truth" for this type of device is defined by engineering standards and established mechanical performance characteristics relevant to spinal implants. Specifically, the ASTM F1717-13 standard dictates the methodologies and expected performance metrics for static and dynamic mechanical integrity. Comparison to legally marketed predicate devices also serves as a benchmark for "ground truth" in terms of acceptable clinical performance and safety.

8. The sample size for the training set

Not applicable. There is no "training set" as this is a mechanical device, not a machine learning algorithm.

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

Not applicable.

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