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The Medyssey Co, Ltd. Zenius™, Iliad™ and Kora™ Spinal Fixation Systems are intended for posterior, noncervical pedicle fixation as an adjunct to fusion in skeletally mature patients using autograft for the following indications: degenerative disc disease (DDD) (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; pseudoarthrosis; and failed previous fusion.

The Zenius™ Spinal System, Internal Fixation Device for Spinal Surgery is comprised of: Rods, Pedicle Screw Assemblies, Compression Retaining Assemblies, and Transverse-Link Assemblies. Various forms and sizes of these implants are available so that adaptations can be utilized to account for the unique pathology of individual patients.

The liad™ Spinal Fixation and Adjustable Bridge System, Internal Fixation Device for Spinal Surgery is comprised of: Rods, Pedicle Screw Assemblies, Compression Retaining Assemblies, and Transverse-Link Assemblies. Various forms and sizes of these implants are available so that adaptations can be utilized to take into account the unique pathology of individual patients.

The Kora™ Spinal Fixation and Adjustable Bridge System, Internal Fixation Device for Spinal Surgery is comprised of: Rods, Pedicle Screw Assemblies, Compression Retaining Assemblies, and Transverse-Link Assemblies. Various forms and sizes of these implants are available so that adaptations can be utilized to take into account the unique pathology of individual patients.

Subject screws are manufactured from Ti6Al4V ELI per ASTM F136. The subject of this submission is the addition of smaller diameter cannulated screws to the spinal systems.

This is a tough one! The provided text is a 510(k) premarket notification for spinal fixation systems (Zenius™, Iliad™, and Kora™). It describes the devices and their indications for use, and makes a claim of "substantial equivalence" to previously cleared predicate devices.

However, a 510(k) submission primarily focuses on demonstrating that a new device is as safe and effective as a legally marketed predicate device. It does not typically involve a clinical study as would be described for an AI/ML device.

The "Performance Testing" section states: "Mechanical testing was performed per ASTM F1717 (Static and Dynamic Compression Bending, Tensile Bending, Static Torsion) to establish that the mechanical properties of the subject devices are equivalent to the predicate devices."

This is bench testing for mechanical properties, not a study involving patient data, ground truth establishment, or expert readers, which are the typical elements requested in your prompt for AI/ML device performance.

Therefore, many of the requested items (sample sizes, data provenance, ground truth, expert qualifications, MRMC studies, standalone studies, training set details) are not applicable to this type of device submission and the performance testing described.

Here's how I can address your request based on the provided text, highlighting the differences:

The provided document describes the Medyssey USA, Inc.'s Zenius™, Iliad™, and Kora™ Spinal Fixation Systems. This is a 510(k) premarket notification, which seeks to demonstrate "substantial equivalence" to already marketed predicate devices, rather than proving efficacy through a de novo clinical study with patient data, ground truth, or human reader performance, as would be the case for an AI/ML device.

The performance testing described is mechanical (bench) testing, not a clinical study involving patient data. Therefore, many of the requested elements are not applicable.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria and performance are for mechanical properties tested against an industry standard, not diagnostic or clinical accuracy. The document states the goal was to "establish that the mechanical properties of the subject devices are equivalent to the predicate devices." It does not explicitly list numerical acceptance criteria or detailed results in the provided excerpt, but refers to ASTM F1717.

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

• Sample Size: Not specified in terms of patient data. The "test set" here refers to physical mechanical samples of the spinal fixation system components (rods, screws, etc.). The exact number of physical samples tested is not stated, but would typically follow ASTM F1717 guidelines.
• Data Provenance: Not applicable in the context of patient data. The "data" comes from physical laboratory testing of manufactured devices.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• Not Applicable. This is mechanical testing of physical devices, not a study evaluating human interpretation or diagnostic accuracy where expert ground truth would be established.

4. Adjudication Method for the Test Set

• Not Applicable. No human interpretation or clinical judgment requiring adjudication was involved in this mechanical testing.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No. An MRMC study is relevant for evaluating the impact of an AI/ML device on human reader performance in diagnostic tasks. This submission is for a physical spinal implant device and relies on mechanical bench testing.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Not Applicable. This device is a physical spinal implant, not an algorithm.

7. The Type of Ground Truth Used

• Not Applicable in the clinical/diagnostic sense. For mechanical testing, the "ground truth" is established by the standardized measurement methods and specifications outlined in ASTM F1717, which define how the mechanical properties (e.g., strength, endurance) are to be determined and compared.

8. The Sample Size for the Training Set

• Not Applicable. This is a physical device, not an AI/ML algorithm that requires a training set.

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

• Not Applicable. As there is no training set for an AI/ML algorithm, there is no ground truth to establish for it.

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