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Thor 700 Electric Handpiece System is intended to convert pneumatic output from a dental treatment center to electrical energy for operation of electrically-driven dental handpiece.

Thor 700 Electric Handpiece System is composed of a system unit, which is connected to a : power supply and a control tube, that system unit drives a dental drive device. The system unit converts pneumatic output from the dental treatment unit to control the torque, speed and directional rotation of the dental drive device. The dental drive device, in turn, provides the control for motorized dental handpieces. The inputs to the system unit are supplied by a dental treatment unit. Inputs can also be controlled by the foot pedal of the treatment unit.

This submission package for the Thor 700 Electric Handpiece System primarily focuses on demonstrating substantial equivalence to a predicate device (Siro Torque L, K031584) through a comparison of technical specifications and non-clinical performance testing. It does not describe an AI/ML-based device, therefore, many of the requested details concerning acceptance criteria and studies for AI performance metrics are not applicable.

Here's an analysis based on the provided document:

Acceptance Criteria and Reported Device Performance (Table)

The document evaluates the Thor 700's performance against established standards and safety requirements, rather than against specific clinically relevant accuracy metrics typically associated with AI/ML devices. The "acceptance criteria" here are generally compliance with international standards and direct comparison of specifications to the predicate device.

Details for AI-Specific Questions (Not Applicable for this Device)

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

• Not Applicable. This is a hardware device (electric handpiece system) and does not involve an AI/ML component or a "test set" of data in the context of diagnostic or predictive performance. The testing involved
  • Electrical safety and EMC testing (IEC standards).
  • Software validation (internal process, not a clinical data set).
  • Performance testing (ISO 11498 for noise, likely empirical measurements of mechanical properties).
  • Sterilization validation (empirical testing in a lab).
  • Biocompatibility testing (animal studies - rabbits).

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. No ground truth in the context of AI/ML classification or prediction was established. "Ground truth" for this device relates to compliance with engineering standards and physiological safety, typically assessed by qualified engineers and toxicologists/biocompatibility experts with appropriate certifications, not medical specialists for diagnostic accuracy.

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

• Not Applicable. No test set requiring expert adjudication for ground truth was used.

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 an electric handpiece system, not an AI-assisted diagnostic or therapeutic tool for human readers. No MRMC study was conducted.

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

• Not Applicable. This is not an algorithm-only device. Software validation was performed, but it pertains to the control and operation of the hardware, not an AI for interpretation or diagnosis.

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

• Not Applicable in the AI/ML sense. The "ground truth" for this device's performance is defined by compliance with established international standards (IEC, ISO, ANSI/AAMI) for electrical safety, EMC, mechanical performance (e.g., speed, torque, noise), software functionality, and biocompatibility. The predicate device's specifications also serve as a comparative benchmark.

8. The sample size for the training set

• Not Applicable. This device does not use an AI/ML model that requires a training set of data.

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

• Not Applicable. No training set was used.

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