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The LED Turbo-Pen is a dental curing light that is designed for use in the optical polymerization of dental resins.

The LED Turbo-Pen consists of a handy unit and a battery charger. The handy unit contains a programmed control circuit, high intensity dental blue LED light source, a light guide and a optical fiber that conduct light to the treatment area on the patient. The control circuit which governs the output power rate. timing and monitoring the temperature of the LED.

The provided text is a 510(k) Premarket Notification summary for the LED Turbo-Pen dental curing light. While it describes the device, its intended use, and compares it to a legally marketed predicate device, it does not contain information about a specific study designed to prove the device meets acceptance criteria.

The section titled "Performance testing has been conducted..." indicates that some testing was performed, but it lacks the detailed methodology, acceptance criteria, results, and other specifics typically found in a study report. The document primarily focuses on establishing substantial equivalence to a predicate device based on technical specifications and intended use.

Therefore, many of the requested elements for describing the acceptance criteria and a study proving compliance cannot be fully answered from this document.

Here's what can be extracted and what cannot:

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

The document does not explicitly state "acceptance criteria" for a study in the format usually requested. However, it does imply performance expectations by comparing the LED Turbo-Pen's specifications to its predicate device, the LED Turbo. The "reported device performance" is essentially the device's measured specifications.

Note: The "acceptance criteria" here are implied by the specifications of the predicate device (LED Turbo) which the LED Turbo-Pen is claiming substantial equivalence to.

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

• Sample Size: Not specified. The document mentions "Performance testing has been conducted" but provides no details on the number of devices or materials tested.
• Data Provenance: Not specified. It does not mention the country of origin of the data or whether it was retrospective or prospective.

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 specified. The document does not describe a study involving expert assessment for establishing ground truth.

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

• Not specified. There is no mention of any adjudication method.

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 a dental curing light, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study is not relevant to this device's function.

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

• This question is generally relevant for AI/software devices. For a dental curing light, its "standalone performance" is its ability to cure resin and activate bleaching agents effectively, which is alluded to but not detailed as a standalone study in the provided text. The document states: "Performance testing has been conducted that confirms that the halogen light technology used for LED Turbo-Pen is able to cure resin and activate tooth bleaching agents within the specified timeframe without causing thermal damage to the tissue." However, no study details are provided.

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

• For a dental curing light, "ground truth" would typically relate to objective measures like the degree of polymerization of dental resins or the efficacy of bleaching material activation, and the absence of thermal damage. The document mentions confirming these aspects but does not describe the specific ground truth measures or how they were established.

8. The sample size for the training set

• Not applicable. This device is a hardware product, not a machine learning model, so there is no "training set."

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

• Not applicable for the same reason as above.

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The LED Turbo is a dental curing light that is designed for use in the polymerization of dental resins and the activation of bleaching materials.

The LED Turbo consists of a handy unit and a battery charger. The handy unit contains a programmed control circuit, high intensity dental blue LED light source, a light guide and a optical fiber that conduct light to the treatment area on the patient. The control circuit which governs the output power rate, timing and monitoring the temperature of the LED.

The provided text describes the 510(k) premarket notification for the "LED Turbo" dental curing light. This submission is for a medical device that cures dental resins and activates bleaching materials, not an AI/ML device, and therefore does not include the typical acceptance criteria and study designs associated with AI/ML device evaluations.

However, based on the information provided, I can construct a table comparing the device's performance against its predicate device, which serves as a form of "acceptance criteria" in the context of a 510(k) submission for substantial equivalence.

Here's an analysis based on the provided text, focusing on how a comparison-based "acceptance" is demonstrated:

1. Table of Acceptance Criteria and Reported Device Performance

For a 510(k) submission, the "acceptance criteria" for a new device are primarily its substantial equivalence to a legally marketed predicate device. This is demonstrated by comparing key technical specifications and functional performance. The table below presents the comparison found in the document.

*Interpretation: The differences in LED power consumption and battery use are presented as technical differences that still achieve equivalent or improved performance (e.g., higher intensity, shorter curing times) while maintaining safety through "Performance testing ... that confirms that the halogen light technology used for LED Turbo is able to cure resin and activate tooth bleaching agents within the specified timeframe without causing thermal damage to the tissue." This "performance testing" is the closest equivalent to a direct study proving criteria.

2. Sample Size Used for the Test Set and the Data Provenance

The document does not describe a traditional "test set" in the context of an AI/ML device where a dataset is analyzed by the algorithm. Instead, it refers to "Performance testing."

• Sample Size: Not specified. The document states "Performance testing has been conducted," but the number of resins, bleaching materials, or in-vitro/in-vivo tests is not provided.
• Data Provenance: Not specified. The text doesn't explicitly state the country of origin of the test data or whether it was retrospective or prospective.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

This information is not applicable to this 510(k) submission. Dental curing lights are physical devices that cure materials; they do not involve human expert interpretation for establishing ground truth in the way AI/ML devices for image analysis do. The "truth" is based on physical material properties and established dental clinical efficacy.

4. Adjudication Method for the Test Set

Not applicable. There is no mention of an adjudication method as would be used for expert review of data for AI/ML validation.

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 is not an AI/ML device and therefore no MRMC study involving human readers with or without AI assistance would have been conducted or reported.

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

Not applicable. This is a physical medical device (dental curing light), not an algorithm.

7. The Type of Ground Truth Used

The "ground truth" for a dental curing light relates to its ability to effectively cure dental resins and activate bleaching agents without causing harm. The document states that "Performance testing has been conducted that confirms that the halogen light technology used for LED Turbo is able to cure resin and activate tooth bleaching agents within the specified timeframe without causing thermal damage to the tissue."

Therefore, the type of "ground truth" would likely be physical measurements and material science validation (e.g., degree of conversion of resin, color change in bleaching agents, temperature measurements to detect thermal damage).

8. The Sample Size for the Training Set

Not applicable. This is not an AI/ML device; there is no "training set."

9. How the Ground Truth for the Training Set was Established

Not applicable, as there is no training set for this type of device.

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