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The inViZia™ Anterior Cervical Plate System is designed for anterior interbody screw fixation of the cervical spine at levels C2-T1.

The inViZia™ Anterior Cervical Plate System is indicated for use in the temporary stabilization of the anterior spine during the development of cervical spinal fusions in patients with degenerative disc disease (as defined by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), trauma (including fractures), tumors, deformity (defined as kyphosis, lordosis or scoliosis), pseudoarthrosis and/or failed previous fusions.

WARNING: This device is not approved for screw attachment to the posterior elements (pedicles) of the cervical, thoracic, or lumbar spine.

The inViZia 10 Anterior Cervical Plate System is a temporary implant system used to facilitate the biological process of spinal fusion. This system consists of plates, screws and instruments and is intended for anterior use in the cervical spine. Surgical instruments are provided to facilitate placement of the implants.

The implants consist of plates of varying length, fixed and variable screws of varying diameter and length. Components are manufactured from titanium alloy (Ti-6Al-4V) that conforms to ASTM F-136.

This system is intended to provide stabilization until a solid spinal fusion develops. The system may then be removed, per the surgeon's discretion.

The Zimmer Spine inViZia Anterior Cervical Plate System is a temporary implant system for spinal fusion.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document describes in vitro mechanical testing rather than a study involving human subjects or patient data. Therefore, there isn't a "test set" in the traditional sense of patient data. The "samples" would refer to the physical devices or components tested. The document does not specify the exact number of devices or components tested for each mechanical test.

• Data Provenance: Not applicable in the context of in vitro mechanical testing. The testing was conducted according to recognized ASTM standards.

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

Not applicable. The ground truth for mechanical performance in this context is established by adherence to recognized industry standards (ASTM F-1717:2010), which are developed by experts in the field of materials science and medical device testing, but not in the sense of clinicians establishing a "diagnosis" or "outcome" as ground truth.

4. Adjudication Method for the Test Set:

Not applicable. The "adjudication method" relates to human interpretation of data, which is not relevant for in vitro mechanical testing against a standard. The results of the mechanical tests would be assessed directly against the pass/fail criteria of the ASTM standard.

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 physical spinal implant, not an AI-powered diagnostic or therapeutic system that would involve human readers or AI assistance.

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.

7. The Type of Ground Truth Used:

The ground truth used for demonstrating device performance is based on engineering standards and mechanical testing specifications (ASTM F-1717:2010). This standard defines the expected mechanical properties and performance characteristics for spinal implant devices.

8. The Sample Size for the Training Set:

Not applicable. There is no "training set" as this is not a machine learning or AI-based device.

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

Not applicable. There is no "training set" as this is not a machine learning or AI-based device.

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