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The OsteoMed Low Profile Plate System is intended to be used in the reconstruction of the floor and/or medial wall of the orbit.

The OsteoMed Low Profile Plate System is indicated for the reconstructive treatment of orbital floor and/or medial wall trauma or bone excision in adult patients.

The OsteoMED Low Profile Orbital Floor Plate System is a permanent implant that is provided non sterile and is manufactured from commercially pure Titanium (ASTM F 67). The OSTEOMED Low Profile Orbital Floor Plates are offered with hole sizes which are able to accept 1.2mm and 1.6mm standard and auto drive bone screws and 1.9mm and 1.9mm safety screws and are offered in thicknesses ranging from .2mm to .5mm.

The implant is also provided in a variety of shapes including both nonpreformed shapes and pre formed left and right profiles. The implant is fixed to the infra orbital rim via either the holes on the provided fixation arms or through the anterior screw holes on the plate itself to reconstruct the orbital floor and/or medial wall in adult patients.

The OsteoMed CFX system which is already available in the marketplace includes the screws (ASTM F136) and instrumentation to facilitate implantation of the Low Profile Orbital Floor Plate System. The instrumentation is made from various grades of stainless steel, anodized aluminum, and/or medical grade plastic. All instruments included in the CFX system are Class I, standard, manual, surgical instruments.

This document describes a 510(k) premarket notification for the "OsteoMed Low Profile Orbital Plate System". This submission focuses on demonstrating substantial equivalence to predicate devices through technical characteristics and mechanical performance. It does not describe an AI medical device, but rather a physical implantable device (bone plate). Therefore, many of the requested categories related to AI performance, such as MRMC studies, training set details, and ground truth establishment for algorithms, are not applicable to this document.

However, I can extract information related to the device's performance testing in a general sense, using the available data:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical "acceptance criteria" for the device's mechanical performance, but rather states that it "meets that of the predicate device". The reported performance is implicitly the successful completion of a verification comparison.

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

The document states "Mechanical testing was performed", but does not specify the sample size used for this mechanical testing. It also does not specify the data provenance (e.g., country of origin, retrospective/prospective). This type of information would typically be detailed in a separate testing report.

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

This is not applicable to a mechanical device testing. "Ground truth" in this context would refer to the physical properties and performance measured, not an expert assessment of an image or data set.

4. Adjudication method for the test set

This is not applicable to mechanical device testing. Adjudication methods are typically used in clinical or image-based studies to resolve discrepancies in expert opinions.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic or screening devices involving human interpretation, which this device is not.

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

This is not applicable. This device is a physical implant, not an algorithm.

7. The type of ground truth used

For mechanical testing, the "ground truth" refers to the measured physical properties and performance characteristics of the device under controlled testing conditions, compared against the known properties of the predicate device. This would likely involve measurements such as strength, durability, and deformation under stress, conducted in a laboratory setting.

8. The sample size for the training set

This is not applicable. There is no "training set" as this is a physical medical device undergoing mechanical testing, not a machine learning algorithm.

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

This is not applicable. There is no "training set" as this is a physical medical device.

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