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DENU Light Body(Regular, Fast) / DENU Heavy Body(Regular, Fast)

• Crown and bridge impression
• Inlay and onlay impression
• Denture impression
• Model impression

DENU Putty Set(Regular, Fast)

• Crown and bridge impression
• Inlay and onlay impression

The DENU family of silicone impression materials consists of three different viscosities (light body, heavy body, putty) for various application systems. Products are further classified into Regular type and Fast type, depending on curing time, but there is no difference on viscosity, indications for use, or scope of use. Each device is consisted of 1:1 base and catalyst component, packaged in 560mL jars for DENU Putty set and 50mL syringes for other devices.

This is a 510(k) premarket notification for a dental impression material, DENU Light Body (Regular, Fast), DENU Heavy Body (Regular, Fast), and DENU Putty Set (Regular, Fast). The submission aims to demonstrate substantial equivalence to legally marketed predicate devices.

Here's an analysis of the acceptance criteria and supporting study information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are primarily derived from the international standard ISO 4823:2021 (Dentistry - Elastomeric impression materials) and the device's performance is compared to these standards and the predicate devices.

2. Sample size used for the test set and the data provenance

The document does not explicitly state the sample sizes for each specific physical property test (e.g., how many samples were tested for working time, consistency, etc.). The testing was based on ISO 4823:2021 and other biocompatibility standards (ISO 7405, ISO 10993-10, ISO 10993-11). These standards typically specify the number of samples required for each test. Given the context of a 510(k) submission, it is assumed that the protocol for each standard was followed, including the specified sample sizes (though not explicitly listed in this summary).

The data provenance is retrospective, as the tests have already been performed and the results are being presented. The manufacturer, HDI, Inc., is based in South Korea, implying the tests were likely conducted there or by affiliated labs.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This submission describes physical and biological bench testing of an impression material against established international standards. There is no mention of human expert involvement or a "ground truth" established by experts in the context of diagnostic interpretation for this type of device. The "ground truth" for these tests is the objective measurement of physical and chemical properties as defined by the ISO standards.

4. Adjudication method for the test set

Not applicable. As described in point 3, this is bench testing of physical and biological properties. There is no expert adjudication process in this context.

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 a medical device submission for a dental impression material, not an AI-powered diagnostic device. No MRMC study was performed.

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

Not applicable. This is a dental impression material, not an algorithm or AI.

7. The type of ground truth used

The ground truth used for this device's evaluation is primarily objective physical and chemical measurements as defined by:

• International Standards: Specifically, ISO 4823:2021 (Dentistry - Elastomeric impression materials) and ISO 10993 series (Biological evaluation of medical devices).
• Predicate Device Performance: The performance characteristics of the proposed device are directly compared to those of the predicate devices (Vonflex STM and Vonflex S Putty).

8. The sample size for the training set

Not applicable. There is no AI or machine learning component to this device, so there is no training set in the conventional sense. The "training" for the device's performance could be considered the iterative formulation and testing by the manufacturer to meet the required specifications, but this is not a data-driven training set like in AI.

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

Not applicable. As there is no training set for an AI/ML algorithm, this question does not apply.

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