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The Intermezzo TM Implant Systems are intended to load immediately in partially or fully edentulous mandibles and maxillae to serve as temporary support for provisional prosthetic device during the healing phase of permanent endosseous dental implants.

Intermezzo ™ Implant System is an integrated system of endosseous dental implants which designed to support prosthetic devices for partially or fully edentulous patients. Intermezzo ™ Implant System consists of Intermezzo ™ Implant Fixtures, Intermezzo Cap, and Implant System Surgery Tray. Implant Fixture Systems consist of one-stage, root-form dental implants, associated with protective cap, which provide the clinician to maintain the patients' gingival contour. The system also includes surgical and restorative instrumentation: lance drills, twist drills, unification drills, and handpiece drivers and hand drivers to provide the clinician to choose only those components required for each clinical situation. The devices covered by this submission are Intermezzo ™ Implant Fixtures, Intermezzo Protective Cap, and Intermezzo Implant System Surgery Tray.

The provided text is a 510(k) summary for a dental implant system (Intermezzo™ Implant Systems). It does not describe an AI/ML device or a study assessing its performance against acceptance criteria in the way envisioned by your request.

The core of this document is to demonstrate "substantial equivalence" of the Intermezzo™ Implant Systems to already legally marketed predicate devices, not to prove its performance against specific acceptance criteria through a clinical study for an AI algorithm.

Therefore, I cannot populate the requested information regarding acceptance criteria and a study that proves the device meets them, specifically for an AI/ML context, because this document describes a traditional medical device (dental implants), not an AI-powered one.

Here's how the provided information relates to your template, highlighting the absence of AI/ML-specific details:

1. Table of acceptance criteria and the reported device performance

• Cannot be provided. The document states: "Laboratory testing was conducted to determine device functionality and conformance to design input requirements." However, it does not specify what those design input requirements (acceptance criteria) were or the results of this testing. The focus is on demonstrating "substantial equivalence" to predicate devices, not meeting novel performance criteria.

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

• Cannot be provided. No test set or data provenance is mentioned in the context of performance evaluation, as this is a traditional medical device submission based on substantial equivalence and laboratory testing, not a clinical study to assess a diagnostic or predictive algorithm.

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)

• Cannot be provided. Ground truth, in the context of AI/ML, refers to definitively established outcomes or labels for a dataset. This concept is not applicable to a submission for a dental implant system.

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

• Cannot be provided. Adjudication methods are relevant for subjective interpretations in clinical trials or for establishing ground truth in AI/ML studies. This document does not describe such a process.

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

• Cannot be provided. No MRMC study was done, as this is not an AI-assisted device.

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

• Cannot be provided. This is not an algorithm.

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

• Cannot be provided. The concept of "ground truth" as it applies to AI/ML clinical validation is not relevant to this device's submission.

8. The sample size for the training set

• Cannot be provided. There is no AI model, therefore no training set.

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

• Cannot be provided. Not applicable due to the absence of an AI model/training set.

In summary: The provided document is a 510(k) summary for a "Class II" dental implant system, demonstrating substantial equivalence to pre-existing predicate devices. It focuses on device description, intended use, materials, and manufacturing, rather than clinical performance data against specific acceptance criteria for an AI/ML algorithm.

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