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For removal of subgingival calculi in periodontal pockets with periodontitis by closed or open curettage. Removal of subgingival calculi in periodontal pockets which have been affected by periodontitis, with open or closed curettage and using the detection function of the KEY Laser 1243.

This device represents the combination into one device of two previously cleared devices, the Diagnodent Laser Fluorescence Caries Detection Device with the KEY Laser III 1243.

Acceptance Criteria and Study for KEY Laser III 1243 US without Feedback, with Detect

Based on the provided 510(k) Summary (K073074), the KEY Laser III 1243 US without Feedback, with Detect is a combination of two previously cleared devices: the Diagnodent Laser Fluorescence Caries Detection Device and the KEY Laser III 1243. The acceptance criteria and "study" are focused on demonstrating that the performance of the individual components has not changed as a result of their combination and that the combined device meets established safety standards for laser and medical devices.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not provide specific details on the sample size used for any in-vitro or bench testing. It only states "Laboratory testing has revealed..." and "bench, and in-vitro studies." The data provenance (e.g., country of origin, retrospective/prospective) is also not specified, as this was a premarket notification for a combination device, and detailed clinical study results are typically not required in the same way as for novel devices.

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

This information is not provided. The "ground truth" for this submission appears to be derived from the established performance and safety profiles of the individual predicate devices and the relevant industry standards. There is no mention of human expert evaluation in establishing ground truth for the "laboratory testing" or "bench/in-vitro studies."

4. Adjudication Method for the Test Set

Not applicable. There is no mention of a test set requiring adjudication in the context of human expert review. The "studies" were technical laboratory and bench tests against pre-defined performance and safety standards.

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 not an AI-assisted diagnostic tool for "human readers" in the typical sense that an MRMC study would be performed. It's a medical device with a laser for surgical procedures and a fluorescence detection function. The comparison is to predicate devices, not to human performance with or without AI.

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

This statement is partially applicable in that the device's functional integrity (laser operation, detection sensitivity) was assessed in isolation. The "laboratory testing" and "bench, and in-vitro studies" essentially evaluated the technical performance of the device components without necessarily considering a "human-in-the-loop" performance study for this type of submission. However, it's not an "algorithm only" device; it's a physical medical instrument. The "detection function" performance was evaluated in what could be considered a standalone manner from the surgical function, but both are physical components of the device.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The ground truth for this submission appears to be:

• Validated performance parameters of the predicate devices: The functionality of the detection and laser surgical components are compared to their previously cleared, individual versions.
• Established industry and regulatory standards: Compliance with various IEC, ISO, UL, VDE, ANSI, and 21 CFR standards for electrical safety, laser safety, and biocompatibility serves as the ground truth for safety and technical performance.

8. The Sample Size for the Training Set

Not applicable. This is not a machine learning or AI algorithm in the context of requiring a training set for model development. The device combines existing, cleared technologies.

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

Not applicable, as this device does not utilize a training set in the AI machine learning sense.

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