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This device is intended for use in:

1. Source of illumination for curing dental restorative materials,
2. Assisting in the whitening process in dental office.

The Easy-Cure™ Curing and Whitening Light is comprised of a high-intensity light source filtered and transmitted through an optical fiber for the curing of composite resins and as an aid in the whitening of teeth. The device emits filtered light in the range from 400 to 500 nm. It accepts voltages from 100-230 VAC.

This looks like a 510(k) submission for a dental curing and whitening light, which is a physical device, not an AI/ML powered software. Therefore, the requested information about acceptance criteria and clinical study results, including details like sample size, ground truth, expert qualifications, and MRMC studies, are not applicable in the context of this specific document.

The document primarily focuses on establishing "substantial equivalence" to existing predicate devices based on its intended use, design, and performance characteristics (like light spectrum and voltage). It does not present a study with performance metrics in the way one would for an AI/ML device.

Here's why the requested information isn't present in this document:

• Acceptance Criteria & Device Performance: The document states "Performance standards: None established under section 514." This means there aren't specific universally mandated performance metrics that the device must meet in the same way an AI algorithm might have. Substantial equivalence is determined by comparing its specifications and intended use to existing, legally marketed devices.
• Sample Size, Data Provenance, Experts, Adjudication, MRMC, Standalone Performance, Ground Truth, Training Set: These are all concepts related to the validation of software algorithms, particularly AI/ML models, where performance is measured against a "ground truth" established by experts on a dataset. A dental light curing device doesn't operate in this manner. Its "performance" is typically assessed through engineering specifications (light intensity, wavelength, safety features) and comparison to predicate devices, rather than statistical analysis on a clinical dataset in the AI/ML sense.

Instead of the requested table, the document achieves its goal by demonstrating:

• Description: The device emits filtered light in the range of 400 to 500 nm and accepts 100-230 VAC.
• Indications for Use: Curing dental restorative materials and assisting in tooth whitening.
• Substantial Equivalence: It lists several predicate devices (e.g., Plasma Arc Curing system, Ardent Radiance Curing Light, Spectrum 800 Curing Unit) that it claims to be substantially equivalent to, based on similar technology, indications, and safety.

In summary, this document is for a traditional medical device (a light source), not an AI/ML software. Therefore, the questions posed about AI/ML device validation are not applicable.

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This device is intended for use in: 1. Source of illumination for curing dental restorative materials, 2. Assisting in the whitening process in dental office.

The device is a standard air-cooled argon laser emitting energy at 457 and 502nm. Output energy is less than 450 mW. The delivery system is a fused silica fiber optic system with a stainless steel handpiece. The laser operates on any 120 volt, alternating current, 20 ampere circuit. The unit is about 16" by 10" by 6.7" in dimension and weighs 20 lbs.

The provided text describes a 510(k) summary for a medical device called CYBER-LASE 2000™, an Argon Laser system. The document focuses on demonstrating substantial equivalence to previously cleared devices rather than presenting a study with acceptance criteria and device performance as one might find for a novel device with clinical endpoints.

Therefore, the requested information elements related to clinical study design, performance metrics, ground truth, experts, and sample sizes for testing and training sets cannot be extracted from this document, as such a study was not the basis for clearance.

However, I can provide the relevant information based on the document's content:

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

The document does not present acceptance criteria and device performance in the manner requested, as it relies on substantial equivalence. The "performance standards" mentioned refer to general regulatory standards for laser products, not specific clinical performance metrics.

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. The device clearance is based on substantial equivalence to predicate devices, not on a clinical performance study with a test set.

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 was established by experts for a test set in this 510(k) submission.

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

Not applicable. No adjudication was performed for a test set.

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 Argon Laser system, not an AI-powered diagnostic device, and no MRMC study was conducted.

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

Not applicable. This device is an Argon Laser system, not an algorithm.

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

Not applicable. The clearance is based on demonstrating the device's technological characteristics and intended use are similar to legally marketed predicate devices, not on clinical performance against a medical ground truth.

8. The sample size for the training set

Not applicable. There is no mention of a training set for an AI/algorithm-based device.

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

Not applicable. There is no mention of a training set or its ground truth.

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