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Spinal Inner Fixation System is intended for posterior, non-cervical, pedicle fixation for the following indications: degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies); spondylolisthesis; trauma (i.e., fracture or dislocation); spinal stenosis; curvatures (i.e., scoliosis, kyphosis and/or lordosis); tumor; pseudarthrosis; and/or failed previous fusion. The device is to be used in skeletally mature patients, and for stabilization of the spine as an adjunct to fusion with bone graft.

The Spinal Inner Fixation System is a medical device system for surgical fixation of the spine. It is made up of pedicle screws, set screw, rods and cross connecting devices. The implanted parts confer stabilization and fusion of two or more spinal segments and are to be removed once their stabilizing function is no longer required. The implants are single-use. The surgical instruments may be reprocessed and re-used. The Spinal Inner Fixation System includes model GB1Z-I and GB1Z-II according to the rod diameter, and each model comprises of different length rods, various length and diameter screws, set screws and accessories. Both models are designed for internal posterior thoracolumbar fixation of the spine. Patient diagnosis and individual conditions should be taken into consideration when selecting the surgical option. Surgical instruments are provided with the device system.

I am sorry, but the provided text from the FDA 510(k) K222031 does not contain information about acceptance criteria or a study that proves a device meets such criteria in terms of AI/algorithm performance.

The document discusses a "Spinal Inner Fixation System," which is a medical device for surgical fixation of the spine, made up of mechanical components like pedicle screws, rods, and connectors.
The "Performance - Bench" section mentions bench performance testing completed according to ASTM F1717-18 standards, including static compression bending, dynamic compression bending, and static torsion. This information pertains to the mechanical properties of the physical device, not the performance of an AI or algorithmic medical device.

Therefore, I cannot provide details on:

1. A table of acceptance criteria and reported device performance for an AI/algorithm.
2. Sample sizes for test sets or data provenance for an AI/algorithm.
3. Number of experts or their qualifications for establishing ground truth for an AI/algorithm.
4. Adjudication methods for an AI/algorithm test set.
5. MRMC comparative effectiveness studies or effect sizes for human readers with and without AI assistance.
6. Standalone performance of an AI algorithm.
7. Type of ground truth used for an AI algorithm.
8. Sample size for a training set for an AI algorithm.
9. How ground truth for a training set was established for an AI algorithm.

The K222031 document is a 510(k) summary for a mechanical orthopedic implant, not an AI-powered medical device.

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Spinal inner fixation system is intended for posterior, non-cervical, pedicle fixation for the following indications: severe spondylolisthesis (grade 3 or 4) of the L5-S1 vertebrae; trauma (i.e. fracture or dislocation), spinal stenosis, curvatures (i.e. scoliosis, kyphosis, and/or lordosis); tumor; pseudoarthrosis; and failed previous fusion. The device is to be used in skeletally mature patients, and for stabilization and immobilization of the spine as an adjunct to fusion with bone graft. The levels of fixation are T8 - S1.

Spinal inner fixation system consists of a variety of shapes and sizes of pedicle screws, rods, hooks, transverse linking pole assembly, and connecting components (side, domino, and axial), which can be rigidly locked into a variety of configurations. These components are made of titanium alloy per ASTM F136.

This document is a 510(k) premarket notification for a medical device, the "Spinal Inner Fixation System." It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving that the device meets a specific set of acceptance criteria for diagnostic performance or treatment efficacy in the way an AI/ML device would.

Therefore, much of the requested information regarding acceptance criteria, device performance, sample sizes for test/training sets, expert ground truth adjudication, MRMC studies, and standalone performance for AI/ML systems is not applicable or present in this document.

However, I can extract the information relevant to the mechanical performance testing conducted for this orthopedic device.

Here's the breakdown of the available information:

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

The document does not explicitly state numerical acceptance criteria for mechanical tests. Instead, it compares the proposed device's performance to that of the predicate device within the context of the testing standard ASTM F1717. The acceptance is based on the results showing no statistically significant difference between the proposed device and the predicate device for various mechanical properties.

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

The document refers to "two samples groups" for the mechanical testing, but the specific numerical sample sizes are not provided. The data provenance is testing conducted by the applicant, Changzhou Dingjian Medical Appliance Co., Ltd., likely in China, as it is the country of origin for the submitter. This is a bench test, not clinical data, so the concepts of retrospective or prospective data as typically applied to patient studies are not applicable.

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 is not applicable as this is a mechanical testing study, not a study involving human interpretation or ground truth establishment by experts in the context of diagnostic performance.

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

This is not applicable for mechanical testing.

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

This is not applicable as this is a mechanical orthopedic device, not an AI/ML diagnostic or assistive device.

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

This is not applicable as this is a mechanical orthopedic device.

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

For mechanical testing, the "ground truth" or reference standard would be the established testing methodology and performance characteristics defined by the ASTM F1717 standard and the performance of the legally marketed predicate device. The comparison is made against the predicate device's measured performance in these standardized tests.

8. The sample size for the training set

This is not applicable as this is a mechanical testing study, not an AI/ML study involving a training set.

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

This is not applicable as this is a mechanical testing study.

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