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This application area extends to endodontic procedures using a root canal instrument which is intended by the manufacturer for use in the mechanical and rotary preparation of root canals.

The motor turned by the power converted into DC2.4V by controller delivers its turning power to the file through spin to perform punching, cutting and removing functions. The hand-piece can be operated, stopped and set/adjusted on/in its speed, torque and turning direction by handling of the controller.

The provided text describes Saeyang Microtech's ENDO a class device, a cordless endodontic handpiece. The 510(k) summary (K123582) details its intended use, substantial equivalence to predicate devices, and the non-clinical tests performed.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" in the way one might expect for a diagnostic or AI device (e.g., sensitivity, specificity thresholds). Instead, the performance is demonstrated through compliance with established international standards and a comparison of technical specifications with predicate devices to establish substantial equivalence.

Based on the provided information, the acceptance criteria relate to compliance with regulatory standards and functional equivalence to predicate devices.

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

The document describes non-clinical bench tests performed to ensure compliance with relevant ISO and IEC standards. These tests typically do not involve "test sets" in the sense of patient data. Instead, they involve testing of physical devices or components against specified technical requirements and protocols.

• Sample Size: Not explicitly stated as a number of devices, but the testing would have been conducted on a representative sample of the ENDO a class device.
• Data Provenance: The tests were conducted internally by Saeyang Microtech in Korea, with results being "Complied" for each standard. Given the nature of bench testing for regulatory submission, this would be considered prospective testing for the purpose of demonstrating compliance.

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 to this submission. The device is a direct drive, AC-powered handpiece for endodontic procedures, not an AI or diagnostic imaging device that requires expert-established ground truth from clinical data. The "ground truth" here is the adherence to engineering and safety standards.

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

This question is not applicable. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies for diagnostic accuracy involving multiple human readers, which is not relevant for this device. The non-clinical tests involved verifying physical and functional specifications against established standards.

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

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-assisted diagnostic tools, not for a dental handpiece. The device is a mechanical tool, and the submission explicitly states: "No clinical testing was conducted."

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

This question is not applicable. The ENDO a class is a physical device, a dental handpiece, not an algorithm or AI system.

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

The "ground truth" in this context refers to compliance with established international standards (ISO, IEC) for medical devices, specifically dental handpieces, and the functional specifications of the device meeting pre-defined thresholds or being comparable to predicate devices. It is based on engineering and performance testing protocols.

8. The sample size for the training set

This question is not applicable. There is no "training set" as this is not an AI or machine learning device.

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

This question is not applicable. There is no "training set."

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