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Chaorum Implant System is intended for use in partially or fully edentulous mandibles and maxilla, in support of single of multiple-unit restorations including; cemented retained, screw retained, or overdenture restorations, and terminal or intermediate abutment support for fixed bridgework. Chaorum Implant System is for single stage and two stage surgical procedures. This system is intended for delayed loading.

Chaorum Implant System are devices made of titanium intended to be surgically placed in the bone of the upper or lower jaw arches to provide support for prosthetic devices such as artificial teeth, and to restore the patients chewing function. Chaorum Implant System consists of fixtures, abutments, and screws. Its material, structure and intended use are substantially equivalent to the predicate devices. It offers three different implants in RBM treatment and SLA treatment.

The provided text describes a 510(k) submission for the "Chaorum Implant System," which is an endosseous dental implant system. This document focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed study proving the device meets specific performance acceptance criteria for an AI/ML algorithm.

Therefore, many of the requested categories about acceptance criteria, study design for AI/ML performance, sample sizes for AI/ML test/training sets, expert ground truth establishment, MRMC studies, or standalone algorithm performance are not applicable to this document. This submission pertains to a physical medical device (dental implants), not a software or AI/ML-driven device.

However, I can extract information regarding the non-clinical testing performed to validate the safety and substantial equivalence of the physical device.

Here's the relevant information that can be extracted, acknowledging that it does not fit the typical format for AI/ML algorithm evaluation:

1. A table of Acceptance Criteria and the Reported Device Performance:

The document does not explicitly state "acceptance criteria" in a quantitative, pass/fail manner for each test, nor does it report specific performance values against those criteria. Instead, it lists the types of non-clinical tests conducted to validate the device's safety and substantial equivalence. The conclusion states that "Based on the similarities and the test results of the validation activities, we conclude that the Chaorum Dental Implant System is substantially equivalent to the predicate device." This implies that the device met the unstated acceptance criteria for each test to support the substantial equivalence claim.

Therefore, for AI/ML-specific criteria, this section is N/A as this is not an AI/ML device.

For the physical device, a table summarizing the types of non-clinical tests performed is provided in the document:

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

N/A. This does not refer to an AI/ML algorithm's test set. The non-clinical tests described involve physical devices and material samples. The document does not specify the number of units or samples used for each physical or biocompatibility test, nor the provenance in the context of "data" (e.g., country of origin, retrospective/prospective). These are laboratory tests on manufactured articles.

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

N/A. This is not an AI/ML device that requires human experts to establish ground truth for image interpretation or diagnosis. The "ground truth" for the non-clinical tests would be the accepted standards and specifications for material science, mechanical engineering, and biocompatibility, as evaluated by engineers and scientists in accredited labs.

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

N/A. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements among human readers in a diagnostic or interpretive task (often in AI/ML performance evaluation). This is not relevant to the non-clinical physical and biological testing of a dental implant.

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:

N/A. MRMC studies are used to evaluate diagnostic systems, often with AI components assisting human readers. This document describes a physical medical device, not a diagnostic system with an AI component.

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

N/A. This is not an AI/ML algorithm; it is a physical dental implant system.

7. The type of ground truth used:

For the non-clinical tests, the "ground truth" is defined by:

• Engineering specifications and design requirements: For dimensional accuracy, mechanical properties, and compatibility.
• Material standards: ASTM F136, ASTM F67 for titanium alloys.
• International standards: ISO10993 series for biocompatibility.
• Sterilization validation protocols: For sterility and shelf-life.

There is no "expert consensus" or "pathology" in the sense of clinical diagnostic ground truth for these types of tests, other than expert evaluation within the relevant engineering and scientific fields to ensure compliance with standards.

8. The sample size for the training set:

N/A. This is not an AI/ML algorithm. There is no concept of a "training set" for the physical device.

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

N/A. As above, no training set is involved.

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