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High-Speed Air Turbine Handpieces:
It is intended for removing carious material, excess filling material, cavity and crown preparation, finishing tooth preparations and restorations, root canal preparations and polishing teeth.

Straight Handpieces:
It is intended for removing carious material, excess filling material, cavity and crown preparation, finishing tooth preparations and restorations, root canal preparations and polishing teeth.

Geared Angle Handpieces:
It is intended for removing carious material, excess filling material, cavity and crown preparation, finishing tooth preparations and restorations, root canal preparations and polishing teeth.

Air Motors:
It is intended for removing carious material, excess filling material, cavity and crown preparation, finishing tooth preparations and restorations, root canal preparations and polishing teeth.

The proposed device is used to hold rotary instruments such as burs and drills for cutting, grinding, and drilling operations. The dental handpieces are hand-held instruments driven by air motor, thus driving the rotation of dental attachments for the purpose of drilling and tooth cutting. Some models include an electric connection to power an LED light

This document describes the regulatory submission for dental handpieces (High-Speed Air Turbine, Straight, Geared Angle Handpieces, and Air Motors). The goal of the submission is to prove the substantial equivalence of the proposed device to existing predicate devices, not to prove clinical safety or effectiveness for a new medical device. Therefore, a traditional "acceptance criteria" and "study that proves the device meets acceptance criteria" as one might see for a diagnostic AI device is not applicable here.

Instead, the submission focuses on non-clinical testing and comparison to predicate devices to demonstrate that the new device is as safe and effective as those already on the market.

Here's an analysis based on the provided document, addressing the prompt's points where applicable, and explaining why others are not relevant in this context:

1. Table of Acceptance Criteria and Reported Device Performance

For this type of device (dental handpiece), "acceptance criteria" are typically defined by compliance with recognized international standards and performance benchmarks relative to the predicate devices. The document does not present a formal "acceptance criteria table" with specific thresholds for outcomes related to a diagnostic or therapeutic AI. Instead, it demonstrates compliance through non-clinical testing against standards and comparison to predicate device specifications.

The "performance" of the device is implied by its adherence to these standards and functional similarity to the predicate.

Here's a table summarizing the differences identified and the justification for substantial equivalence, which acts as the "performance" verification in this context:

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

• Test Set: Not applicable for a traditional "test set" in the context of an AI/diagnostic algorithm. The "testing" here refers to bench testing (non-clinical) of physical devices against engineering specifications and international standards. The sample size would refer to the number of physical units tested, but this detail is not provided in a P(K) summary, as it's standard engineering verification.
• Data Provenance: Not applicable in the sense of patient data. The "data" comes from engineering measurements and tests performed on the physical dental handpieces.

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

• Not applicable. Ground truth, in this context, is established by adherence to international engineering standards (e.g., ISO, IEC, ANSI/AAMI) and direct comparison of physical properties with legally marketed predicate devices. No human expert consensus on medical image interpretation or clinical outcomes is needed for this type of device.

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

• Not applicable. This concept pertains to expert review and consensus in diagnostic studies, which is not relevant here.

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:

• Not applicable. This device is a mechanical dental instrument, not an AI or diagnostic tool that assists human readers. No MRMC study was performed. The "Clinical Test Conclusion" (Page 6, Section 8) explicitly states: "No clinical study is included in this submission."

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

• Not applicable. This is not an algorithm; it's a mechanical device.

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

• The "ground truth" for this submission is based on:
  • International Standards: Adherence to established standards such as ISO 14457 (Dentistry - Handpieces and motors), IEC 60601 series (Medical electrical equipment), ISO 10993 series (Biological Evaluation of Medical Device), etc.
  • Predicate Device Specifications: Direct comparison of the proposed device's specifications and performance to those of the legally marketed predicate devices (K181691 and K202786). The argument for "Substantial Equivalence" is built on demonstrating that any differences do not raise new questions of safety or effectiveness.
  • Bench Testing Results: Data derived from physical performance tests (e.g., bur extraction force, speed measurements, biocompatibility tests).

8. The sample size for the training set:

• Not applicable. There is no "training set" as this is not an AI/machine learning device.

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

• Not applicable. No training set for an AI model.

In summary:

This FDA 510(k) submission for dental handpieces is a pre-market clearance based on substantial equivalence to existing devices. The "acceptance criteria" are met by demonstrating compliance with recognized performance standards and by showing that any differences from the predicate devices do not introduce new safety or effectiveness concerns, primarily through non-clinical bench testing and adherence to international engineering and safety standards. Clinical studies or AI-specific performance metrics (like MRMC, standalone algorithm performance, or expert ground truthing) are not required or relevant for this type of device submission.

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