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nuance® FLOW is intended to be used as a composite tooth restorative.

nuance® FLOW is recommended for the following types of applications:

1. Direct restorations of anterior or posterior teeth
2. Cavity base/liner
3. Intraoral repairs of fracture crowns/bridges

nuance® FLOW is a Class II medical device intended to be used as a composite tooth restorative. According to the applicable FDA recognized consensus standard, ISO 4049-"Dentistry- Polymer-based restorative materials", this device is classified as Class 2: materials whose setting is affected by the application of energy from an external source, such as blue light or heat (Group 2). nuance® FLOW is DenMat's Flowable for use in direct restorations of anterior or posterior teeth. They are polymerizable dental monomer resins that are chemically-cured when exposed to dental curing lights. It is used to form a durable, esthetic restoration. It is a lower viscosity material that allows it to be used when ease of application and thinner layers are needed. nuance® Flow may be appropriate for dental Class I, III, IV and V type restorations where maximum mechanical strength and wear resistance is not required. A small amount of fluoride (.5-.7%) is included to minimize likelihood of secondary caries. nuance® FLOW is packaged in a 1 mL single• barrel syringe and offered in 8 shades: A1, A2, A3, A3.5, B1, B2, BL1 and BL2.

This document describes the 510(k) premarket notification for the nuance® FLOW dental restorative material, and its substantial equivalence to the predicate device LC FLOWFILL.

Here's an analysis based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify the exact sample sizes used for each test performed on nuance® FLOW or LC FLOWFILL. It only states that "Testing results indicate that nuance® FLOW was as effective and performs as good as the predicate device."

The data provenance is not explicitly stated in terms of country of origin or retrospective/prospective. However, given that it's a 510(k) submission to the FDA, it is expected that the tests were conducted according to recognized international standards (ISO 4049) which implies controlled laboratory settings. The document also mentions "Scientific literature have been evaluated to support the use of similar products for the same indications," suggesting some reliance on existing published data.

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

This information is not provided in the document. The tests are bench tests based on an international standard (ISO 4049), not clinical evaluations requiring expert interpretation of results for ground truth.

4. Adjudication method for the test set

This information is not applicable as the tests are objective, quantitative measurements against a standard, not subjective interpretations requiring adjudication.

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

There was no MRMC comparative effectiveness study and no mention of AI assistance. This device is a dental restorative material, not an AI-powered diagnostic tool.

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

This question is not applicable as the device is a physical dental material and not an algorithm.

7. The type of ground truth used

The ground truth used for performance evaluation is based on the objective measurement criteria defined in the ISO 4049 standard for "Dentistry – Polymer-based restorative materials." This standard sets specific quantitative thresholds for various material properties.

8. The sample size for the training set

This information is not applicable as the device is a chemical material, not a machine learning algorithm that requires a training set.

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

This question is not applicable as the device is a chemical material, not a machine learning algorithm.

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