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The Elos Accurate® Customized Abutments are intended for attaching to dental implants in order to provide basis for single or multiple tooth prosthetic restorations. The Elos Accurate® Customized Abutment will be attached to a dental implant using the included Elos Prosthetic screw. The Elos Accurate® Customized Abutments are compatible with the implant systems listed in Table 1.

The Elos Accurate® Customized Abutment is a patient specific abutment intended for attaching to dental implants in order to provide basis for single- or multiple tooth prosthetic restorations. The Elos Accurate® Customized Abutment will be attached to the implant using the included Elos Prosthetic Screw and attached to the crown/coping manually by cementation. The Elos Accurate® Customized Abutment consists of an Abuttnent Blank used in fabricating of a full patient-specific abutment in Titanium alloy per ASTM F136. The Abutment Blank used in creation of the Elos Accurate® Customized Abutment has a pre-manufactured connection interface that fits directly to a pre-specified dental implant. The customized shape of the abutument is intended to be manufactured according to a digital dentistry workflow or intended to be manufactured at an FDA registered Elos Medtech approved milling facility. The Elos Accurate® Customized Abutment is delivered non-sterile and the final restoration including corresponding Elos Prosthetic Screw is intended to be sterilized at the dental clinic before it is placed in the patient. The Elos Accurate® Customized Abutment provides clinicians and laboratories with a prosthetic device that can be used in definitive (permanent) single- or multi restorations.

The provided document, K231307 for Elos Accurate® Customized Abutment, is an FDA 510(k) premarket notification. This document focuses on demonstrating substantial equivalence to previously cleared devices rather than presenting a standalone study with defined acceptance criteria and performance metrics in the way a clinical trial or a performance study for an AI/ML powered medical device would.

The "acceptance criteria" discussed in this document pertain to the device meeting the requirements for FDA 510(k) clearance by demonstrating substantial equivalence to predicate devices, primarily through engineering, dimensional, and non-clinical testing. There is no mention of a human-in-the-loop study, a multi-reader multi-case (MRMC) study, or an AI/ML algorithm-only standalone performance study.

Therefore, many of the requested fields regarding expert adjudication, MRMC studies, and ground truth establishment for AI/ML models are not applicable to the information contained within this 510(k) submission.

Here's an interpretation of the document's contents in relation to your request, with a focus on non-clinical testing and substantial equivalence:

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

The "acceptance criteria" for this device are implicitly tied to demonstrating substantial equivalence to its predicates and meeting established material and dental implant abutment standards. The document doesn't present a table with numerical acceptance criteria and performance for a diagnostic AI algorithm. Instead, it lists various non-clinical tests and their successful outcomes as evidence of substantial equivalence and safety/effectiveness.

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

• Test Set Sample Size: The document does not specify exact "sample sizes" in terms of number of abutments or implants tested for each reported non-clinical test. The tests are general performance validations (e.g., fatigue, cytotoxicity, sterilization efficacy) and dimensional analyses that demonstrate compliance with relevant standards or a worst-case scenario. For example, fatigue testing often involves a small number of samples (e.g., 6 minimum per ISO 14801, though more are often used for statistical power) under specific loading conditions. Similarly, biocompatibility is performed on representative samples.
• Data Provenance: The data comes from the manufacturer's (Elos Medtech Pinol A/S) internal testing and leverages data from previously cleared devices where material, size, and geometry are substantially equivalent. The provenance is therefore the manufacturer's testing facilities and associated regulatory submissions. There is no indication of different countries of origin for the data concerning these non-clinical tests. The tests are prospective in nature, as they are part of the premarket submission process.

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

This question is not applicable. The "ground truth" for this medical device (dental abutment) is established by engineering specifications, material properties, performance standards (e.g., ISO, ASTM), and clinical safety and effectiveness data from the predicates. There is no AI/ML component described that would require expert human review or "ground truth" establishment in the context of diagnostic interpretation.

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

This question is not applicable as there is no human interpretation or diagnostic "test set" that would require adjudication.

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 question is not applicable. The device is a physical dental abutment and its associated digital design workflow, not an AI-powered diagnostic tool requiring human reader studies.

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

This question is not applicable. The device is not a standalone AI algorithm. The digital dentistry workflow involves CAD software and CAM software, but these are tools for designing and manufacturing the physical abutment, not for automated diagnostic interpretation or decision-making in the clinical sense of an AI/ML algorithm.

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

The "ground truth" for this device's performance, as demonstrated in the submission, is based on:

• Compliance with international standards (e.g., ISO 14801 for fatigue, ISO 10993-5 for biocompatibility, ISO 17665 for sterilization).
• Engineering and dimensional analysis against established dental implant system specifications.
• Physical testing results (e.g., torque, heating, displacement) for MR compatibility.
• Validation of the digital workflow ensuring design constraints and manufacturing accuracy.
• The fundamental demonstration is substantial equivalence to existing legally marketed predicate devices, implying that their established safety and effectiveness forms the basis of the "ground truth" for this device's intended use.

8. The sample size for the training set

This question is not applicable. This is not an AI/ML device that requires a training set for model development. The design software has built-in constraints ("design limitations" and "design limits in the library file"), which are more akin to pre-programmed rules and geometric parameters rather than a learned model from data.

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

This question is not applicable. No AI/ML training set is mentioned or implied. The "ground truth" for the design limitations within the software (e.g., minimum material thickness) would be established by engineering principles, biomechanical studies (often in labs, not clinical trials), and clinical experience with dental prosthetics, which define safe and effective design parameters.

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