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The Prep Technology Corp. PrepTech 200TM Cavity Preparation System is intended for use in cavity preparation for bonded restorations, the removal of decayed or sound tooth structure and composite restoration materials and for the preparation of tooth surfaces prior to pit and fissure sealant and composite restorations.

The Prep Technology Corp. PrepTech 200 Cavity Preparation System is a device that combines pressurized air and aluminum oxide powder to produce a high velocity stream of kinetic particles to perform dental restorative procedures, including preparation for pit and fissure sealants and composite restorations.

The provided text is a 510(k) summary for the Prep Technology Corp. PrepTech 200TM Cavity Preparation System. This type of document is a premarket notification to the FDA to demonstrate that the device to be marketed is at least as safe and effective as a legally marketed device (predicate device). It primarily focuses on comparing the new device to predicate devices and establishing substantial equivalence, rather than detailing a specific study to prove the device meets acceptance criteria in the way a clinical trial report would.

Therefore, the sections of your request related to specific study design, performance metrics, ground truth establishment, expert qualifications, and sample sizes for training and test sets are not directly addressed in this 510(k) summary. The summary focuses on the intended use, technological characteristics, and comparison to predicate devices, not on quantitative performance outcomes from a specific clinical study for acceptance criteria.

However, I can extract the relevant information that is present:

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

The 510(k) summary does not provide a table of acceptance criteria with corresponding performance statistics. The device demonstrates "substantial equivalence" to predicate devices, which is the regulatory acceptance criteria for a 510(k) submission.

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

This information is not provided in the 510(k) summary. The submission focuses on comparing the device's technological characteristics and intended use to predicate devices, not on a specific clinical study with a test set.

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 information is not provided. Ground truth establishment for a test set is not detailed in this type of regulatory submission.

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

This information is not provided. Adjudication methods for a test set are not detailed in this type of regulatory submission.

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

A multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned. This device is not an AI-assisted diagnostic tool; it is a physical dental instrument. Therefore, the concept of "human readers improve with AI vs. without AI assistance" does not apply.

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

This device is a physical instrument, not an algorithm. Therefore, a "standalone algorithm performance" study is not applicable. The device operates with human involvement (dentist) to perform its intended functions.

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

The concept of "ground truth" as typically applied to performance studies for diagnostic algorithms is not relevant here. The substantial equivalence is based on comparing the device's technical specifications and intended use against legally marketed predicate devices, not against an absolute ground truth of physiological or pathological conditions.

8. The sample size for the training set

This information is not provided. The device is a physical product, not a machine learning model, so there is no "training set."

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

This information is not provided, as it is not applicable to this type of device.

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