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The Internal Octa Implant System is intended for placement in the maxillary and/or mandibular arches to support crowns, bridges, or overdentures in edentulous or partially edentulous patients. The Internal Octa Implant System is intended for immediate loading when good primary stability is achieved and with appropriate occlusal loading.

An endosseous dental implant is a device made of Pure Titanium Grade 4. Internal Octa Implant System has been designed to accommodate the following dental implant restoration protocols; Immediate or Early loading, immediate placement or one or two stage placements. Internal Octa Implant Systems help patients who have partial or whole teeth loss mastication to chew as dental implant. The Internal Octa Implant System has an internal connection. The surface of the system has been treated with SLA (Sandblast, Large grit Acid etched).

There are 3different kinds of fixtures, BLT III, Precision III and LISA Implants. The only differences between these implants are designs and dimensions. The appearance of implants is different depending on the presence of cutting edge and taper. The Fixtures are supplied sterile.

This document describes the regulatory approval for the "Internal Octa Implant System," a dental implant. It is a 510(k) submission, meaning the manufacturer is demonstrating that their new device is "substantially equivalent" to existing, legally marketed devices (predicates).

Therefore, the performance data presented here is related to demonstrating substantial equivalence, primarily through bench testing and comparison to predicates, rather than extensive clinical studies or AI model performance as would be seen for a novel diagnostic AI device.

Given that this document is for a dental implant system, and not an AI-based diagnostic device, it does not contain the information required to populate a table of acceptance criteria for AI performance metrics (like sensitivity, specificity, AUC), nor does it describe a study involving human readers or AI in the way your prompt defines. The performance data provided is focused on material biocompatibility and physical characteristics of the implant.

Let's break down why this document doesn't fit your prompt's requirements:

• No AI Component: The document describes a physical medical device (dental implants made of titanium). There is no mention of artificial intelligence, diagnostic algorithms, or image analysis performed by an AI.
• No Diagnostic Performance Metrics: Given the above, there are no references to sensitivity, specificity, AUC, or other metrics typically associated with AI diagnostic performance.
• No Human Reader Studies: Since it's a physical implant, there are no studies detailed that involve human readers (like radiologists) interpreting images with or without AI assistance.
• Ground Truth: The "ground truth" for a dental implant would be related to its physical properties, biocompatibility, and intended function in the human body, not an expert consensus on image interpretation.
• Training/Test Sets for AI: These concepts are irrelevant as there is no AI model being trained or tested.

However, to answer your prompt in the context of this specific document, I will interpret "acceptance criteria and reported device performance" as the basis for substantial equivalence for this medical device, and then address the other points by stating their inapplicability to this type of device submission.

Acceptance Criteria and Reported Device Performance (as applied to Substantial Equivalence for a Dental Implant):

For this Traditional 510(k) submission, the "acceptance criteria" revolve around demonstrating that the Internal Octa Implant System is as safe and effective as its predicate devices. This is achieved through various forms of bench testing and comparison of physical and material characteristics.

1. Table of Acceptance Criteria and Reported Device Performance (Interpreted for a Dental Implant):

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

• This document does not describe a "test set" in the context of an AI model evaluating data.
• For the bench testing (biocompatibility, surface characterization, endotoxin), the sample sizes would refer to the number of individual implant units or material samples tested. These specific numbers are not provided in this summary document but would be detailed in the full test reports referenced (e.g., in the ISO 10993 reports).
• Data Provenance: The bench testing would have been conducted in a laboratory setting, likely affiliated with the manufacturer (EBI Inc. in Republic of Korea) or a contract testing organization. The data is prospective in the sense that the tests were performed specifically for this submission.

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

• This concept is not applicable to this submission. "Ground truth" for a dental implant is established through engineering specifications, material science standards (ASTM, ISO), and regulatory requirements, not through expert consensus on images.
• The judgment of substantial equivalence is made by reviewers at the FDA based on the submitted technical data and comparison to predicates.

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

• This concept is not applicable to this submission, as there is no "test set" requiring adjudication by multiple readers or experts for diagnostic accuracy.

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 MRMC study was done. This is a submission for a physical dental implant, not an AI-assisted diagnostic device. Therefore, there is no "human-in-the-loop performance" or "effect size of human readers improving with AI" relevant to this document.

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

• No standalone algorithm performance was done. This document describes a physical medical device, not a software algorithm.

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

• The "ground truth" or standard for acceptance here is compliance with pre-defined engineering standards (ASTM, ISO), material properties, and biological compatibility as demonstrated through standardized bench testing. It's essentially a "comparison to technical specifications and regulatory standards" truth.

8. The sample size for the training set:

• Not applicable. There is no AI model being trained, so no training set exists.

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

• Not applicable. As there is no AI model, there is no training set and therefore no ground truth to be established for it.

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