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SM-Extra Wide(RBM) Implant Fixture is intended to be surgically placed in the maxillary or mandivular molar areas for the purpose of providing prosthetic support for dental restoration (Crown, Bridges, and overdentures) in partially or fully edentulous individuals. These Fixtures can be used where smaller implants have failed.

SM-Extra Wide(RBM) Implant System consists of SM-Extra Wide(RBM) fixtures, abutments, prosthetics and surgical instruments.

SM-Extra Wide(RBM) Implant Fixtures are made of commercial pure titanium, grade 4 which have a sand-blasted, RBM(Resorbable Blast Media) treated surface. These fixtures are the onestage implant and two-stage implant and surgically inserted in the maxillary or mandibular molar areas or where smaller implants have failed.

These fixtures are the integrated system of endosseous dental implants which designed to Provide prosthetics support for dental restoration (Crown, Bridges, and overdentures) in partially or fully edentulous individuals.

The screw, cemented and overdenture retained restoration, other superstructure and instruments for prosthetics that used when the SM-Extra Wide implants is surged are same with each standard type of DIO SM Implant System.

This submission, K080128, for the SM-Extra Wide(RBM) Implant System, does not describe an AI/ML powered device. Instead, it is a traditional 510(k) submission for a dental implant system. As such, the information you've requested regarding acceptance criteria and studies proving the device meets those criteria, particularly with reference to AI/ML specific performance metrics, is not present in the provided text.

The submission focuses on establishing substantial equivalence to a predicate device (Rescue Internal Dental Implant System, K063216) based on technological characteristics and intended use.

Here's an analysis of the provided text in relation to your questions, highlighting why most AI/ML-specific questions are not applicable:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria: The document implies acceptance criteria are based on device functionality and conformance to design input requirements, as stated in section 8: "Laboratory testing was conducted to determine device functionality and conformance to design input requirements." However, specific numerical acceptance criteria (e.g., success rates, torque values, fatigue limits) for these "design input requirements" are not explicitly listed in the provided text.
• Reported Device Performance: The primary "performance" reported is the demonstration of substantial equivalence to the predicate device. This is shown through a direct comparison of technological characteristics in Section 12. No quantitative performance data from clinical trials or specific laboratory tests (beyond the general statement above) are provided or summarized.

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

• This information is not applicable as this is a physical medical device (dental implant) and not an AI/ML algorithm or software that operates on data. The "testing" mentioned is laboratory testing of the physical implant's functionality, not a test set of data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• This is not applicable. Ground truth, in the context of expert consensus, is relevant for evaluating the performance of diagnostic or prognostic AI/ML models. For a physical dental implant, "ground truth" would be established through established engineering and biocompatibility standards, not expert adjudication of a test set.

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

• This is not applicable. Adjudication methods are used in studies involving human interpretation or consensus for data labeling/ground truth in AI/ML performance evaluation.

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

• This is not applicable. MRMC studies are specific to evaluating diagnostic systems, particularly imaging devices or AI tools, and their impact on human reader performance. This submission is for a physical implant.

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

• This is not applicable. This refers to the standalone performance of an AI/ML algorithm.

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

• For the performance of this physical implant, the "ground truth" implicitly relies on engineering standards, material science properties, biocompatibility testing (which may involve in-vitro or animal studies, though not detailed here), and successful historical clinical use of physically similar predicate devices. No explicit "ground truth" of the types listed (expert consensus, pathology, outcomes data) for a data-driven evaluation is mentioned.

8. The sample size for the training set

• This is not applicable. There is no AI/ML algorithm being "trained" in this submission.

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

• This is not applicable. There is no AI/ML algorithm being "trained" in this submission.

In summary, the K080128 submission is a standard 510(k) for a physical medical device, demonstrating substantial equivalence to a predicate. The questions posed are primarily relevant to AI/ML powered devices, which this product is not.

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