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This product is intended only for dental clinic /dental office use. This device generates ultrasonic waves intended for use in dental applications such as scaling, root canal treatment, periodontal and cavity preparation.

The Varios 370 is a compact, portable control unit powered by the iPiezo® engine. The product comes with a wide range of tip inserts, which can be attached at the distal end of the Varios 2 Handpiece transducer and vibrates at ultrasonic frequencies of 28 to 32 KHz. The Varios 370 LUX features twin LED lights, assuring generally clearer vision and easier identification of the treatment area.

This report does not contain information about the acceptance criteria and the study proving the device meets those criteria from an AI/ML perspective. The document is a 510(k) summary for an ultrasonic scaler (Varios 370 / Varios 370 Lux) and focuses on regulatory compliance, outlining the device's description, intended use, technological characteristics, and a summary of testing against applicable industry standards (e.g., electrical safety, biocompatibility, sterilization).

Here's a breakdown of what is provided, and why it doesn't align with the requested AI/ML-centric information:

What is provided in the document:

• Device: Varios 370 / Varios 370 Lux (Ultrasonic Scaler)
• Intended Use: Dental clinic/office use for applications like scaling, root canal treatment, periodontal and cavity preparation using ultrasonic waves.
• Summary of Testing: The document states the device underwent "design validation, including software validation" as required by 21 CFR 820.30(g) and was tested in accordance with several standards:
  • IEC 60601-1 (Electrical Safety)
  • UL 60601-1 (Electrical Safety)
  • IEC 60601-1-2 (Electromagnetic Compatibility)
  • ISO 10993-1 (Biocompatibility)
  • ISO 22374 (Dental handpieces Electrical-powered scalers and scaler tips)
  • AAMI/ANSI/ISO 17665-1 (Sterilization)
• Predicate Device: K031421 – Nakanishi Varios 350 / Varios 350 Lux, to which the current device is considered "substantially equivalent."
• Conclusion: Substantial equivalence is based on similarities in primary intended use, principles of operation, design rationale, test results, and performance.

Why this doesn't fit the requested AI/ML acceptance criteria study:

The provided document describes a traditional medical device (an ultrasonic scaler) and its regulatory submission. It does not mention any AI or Machine Learning components. Therefore, there are no AI/ML acceptance criteria, no studies on algorithm performance, no discussions of training or test sets, no data provenance related to AI, no ground truth establishment for AI, and no MRMC studies or standalone algorithm performance metrics.

To answer your specific questions based on the absence of AI/ML in this document:

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

   • Acceptance Criteria (for regulatory submission): The device successfully met the requirements of the standards listed (IEC 60601-1, UL 60601-1, IEC 60601-1-2, ISO 10993-1, ISO 22374, AAMI/ANSI/ISO 17665-1) and successfully underwent design validation, including software validation, as per 21 CFR 820.30(g). Performance was also deemed similar to the predicate device.
   • Reported Device Performance (for AI/ML): Not applicable, as there is no AI/ML component.

2. Sample sized used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable for AI/ML. The device's performance was likely evaluated through engineering tests (e.g., electrical safety, EMC, vibration frequency, biocompatibility tests) rather than a clinical "test set" in the context of data analysis for AI.

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): Not applicable for AI/ML. Ground truth, in this context, would relate to the physical and functional specifications of the device meeting its design intent and safety standards, rather than expert interpretation of data.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable for AI/ML.

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, as there is no AI component.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable, as there is no AI component.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): The "ground truth" for this device would be defined by the technical specifications, performance standards, and safety requirements outlined in the referenced IEC/ISO/UL standards. For example, for electrical safety, the ground truth is whether the device adheres to leakage current limits. For biocompatibility, it's whether the materials are non-toxic.

8. The sample size for the training set: Not applicable, as there is no AI/ML component.

9. How the ground truth for the training set was established: Not applicable, as there is no AI/ML component.

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