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The Avante LED curing light is used as a light activation system for dental restorative resins.

The Avante LED curing light performs the same intended function as its predicate device, Versalux (reference K010133). Both devices are both used as a light activation system for dental restorative resins.

The physical features of the Avante LED curing light provide the dentist with a compact shape and cordless design to facilitate ease of use while using standard light emitting diode technology for optimum curing functions. The Avante LED curing light provides minimum output of 1.000 MW/cm2 which results in typical curing times of 10 seconds for most composite materials. Peak output is measured at 470nm. Curing time can be set between 1- 40 seconds for greater versatility in individual applications.

Accessories include the Avante light guides which are available in four diameters to accommodate dentist preference and use.

The effective portability of the Avante LED curing light is maintained through use of a charging light which is incorporated into the device stand. Included as well in the charging light is a radiometer display to verify proper light output. Lithium-ion batteries power the device and provide 45 minutes on average between charging times.

This submission is for a medical device (Avante LED curing light), not an AI/ML device. Therefore, the requested information regarding acceptance criteria and study details for AI/ML performance is not applicable. The provided text describes the device's function, physical features, and substantial equivalence to a predicate device, but does not include information about clinical trials or performance metrics in a way that would directly translate to the requested AI/ML-specific criteria.

The submission focuses on establishing substantial equivalence to a legally marketed predicate device (Versalux, K010133) for its intended use as a light activation system for dental restorative resins.

However, I can extract the device's reported performance characteristics from the provided text:

Device Performance Information Extracted from the Text:

Since the request is about acceptance criteria and study that proves the device meets the acceptance criteria and the context is a 510(k) submission for a non-AI/ML medical device, the "study" demonstrating it meets acceptance criteria for this type of device is typically a device comparison/equivalence study rather than a clinical outcome study. The provided document states:

• "The Avante LED curing light performs the same intended function as its predicate device, Versalux (reference K010133)."
• "A review for safety and effectiveness was performed and found not to have been affected."
• "We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices..."

This indicates that the device was deemed acceptable due to its substantial equivalence to a predicate device, rather than through a a trial with a defined test set, experts, and ground truth as would be relevant for an AI/ML device.

Therefore, for the specific questions related to AI/ML device validation:

1. A table of acceptance criteria and the reported device performance: See table above for device performance. Acceptance criteria for this type of device would generally relate to performance specifications (light output, wavelength, curing time range, battery life) and safety, largely demonstrated through engineering testing and comparison to the predicate. The document doesn't explicitly list "acceptance criteria" values against which performance is measured, but rather states the device's capabilities.
2. Sample size used for the test set and the data provenance: Not applicable for this type of device submission's "study."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable for this type of device submission's "study."
4. Adjudication method: Not applicable.
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.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used: Not applicable.
8. The sample size for the training set: Not applicable.
9. How the ground truth for the training set was established: Not applicable.

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