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Young Silver Fluoride Hypersensitivity Varnish is indicated for the treatment of dental hypersensitivity in adults. It is intended for professional use only.

Young Silver Fluoride Hypersensitivity Varnish is a formulation of silver, sodium fluoride, chitosan, acetic acid, and purified water intended for the treatment of dental hypersensitivity in adults. It is intended for professional use only.

Like other 510(k) cleared dental hypersensitivity varnishes including the predicate device, the proposed device works by physically occluding dentinal tubules through its silver and fluoride components. It contains small silver particles to facilitate their entry into the dentinal tubules, as well as chitosan and acetic acid to evenly disperse the solution. The product is supplied in a 10mL bottle with a dropper dispensing tip.

One to two drops of the varnish can treat up to 8 sites. If needed, a second application of the varnish may be administered after 1 week.

The provided text describes a 510(k) submission for a dental varnish. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than undergoing a full clinical efficacy study with acceptance criteria often associated with AI/software medical devices. Therefore, the information needed to answer the questions about acceptance criteria for AI/software device performance is not present in this document.

The document discusses performance testing in a general product development sense (e.g., bench testing, biocompatibility, shelf life) to show the new device performs "as well or better than" the predicate, but these are not the quantitative acceptance criteria for AI model performance (e.g., sensitivity, specificity, AUC) that the prompt is looking for.

Specifically, there is no mention of:

1. Acceptance criteria table for AI performance: The document does not define specific metrics (e.g., sensitivity, specificity, accuracy) for an AI model.
2. Study proving device meets acceptance criteria: There is no clinical study described that evaluates an AI model's performance on a test set. The "study" mentioned refers to bench testing (dentinal occlusion, ion release) and biocompatibility for a physical dental varnish.
3. Sample size for a test set or data provenance: No test set of patient data for AI model evaluation is discussed.
4. Number of experts or qualifications for ground truth: No expert review for labeling images/data for AI training or testing is mentioned.
5. Adjudication method for a test set: Not applicable as there's no AI test set.
6. MRMC comparative effectiveness study: No study involving human readers with and without AI assistance is described.
7. Standalone (algorithm only) performance: Not applicable as it's not an AI algorithm.
8. Type of ground truth used: For a physical dental varnish, ground truth would relate to its physical and chemical properties, not diagnostic outcomes based on expert consensus or pathology data for an AI.
9. Sample size for training set, or how its ground truth was established: Not applicable as there's no AI training set.

In conclusion, the provided document does not contain the information requested because it pertains to a chemical dental product (varnish) and its 510(k) clearance based on substantial equivalence, not an AI/software medical device.

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The DEFEND Ear-Loop Face Masks are intended to be worn to protect both the patient and healthcare professional from transfer of microorganisms, body fluids, and particulate material. These face masks are intended for use in infection control practices to reduce the potential exposure to blood and body fluids. This is a single use, disposable device, provided non-sterile.

The DEFEND Ear-Loop Face Masks are comprised of four layers of nonwoven materials ultrasonically welded together along the edges with ear loops ultrasonically welded to the sides. The four layers include an outer layer (faces outward when worn), filtration layer, middle layer, and inner layer (faces the user). The materials used in this mask meet the performance requirements for Level 3 stated in ASTM F2100 -19 Performance of Materials Used in Medical Face Masks. The mask is secured to the face with ear loops. The mask also includes a form-fitting pliable nose piece to improve fit and reduce gaps for maximum protection.

The provided document describes the K201127 submission for DEFEND Ear-loop Face Masks, a Class II surgical mask. The information focuses on non-clinical performance testing against ASTM F2100 standards to demonstrate substantial equivalence to a predicate device.

Here's an analysis of the acceptance criteria and the study that proves the device meets those criteria, based on your requested categories:

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

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

• Sample Size for performance tests (ASTM F1862, F2101, F2299, MIL-M-36954C/EN 14683): "32/32 Pass per lot" is mentioned for these tests. This implies that 32 samples were tested per lot, and all passed. The document does not specify how many "lots" were tested.
• Sample Size for biocompatibility tests (ISO 10993-5, 10993-10): The sample size for these specific tests is not explicitly stated in the provided text, but the results indicate that the tests were performed "Under the conditions of the study".
• Data Provenance: The document does not specify the country of origin of the data. It is a submission to the U.S. Food & Drug Administration (FDA), suggesting the data was generated for the purpose of meeting U.S. regulatory requirements. The studies are retrospective in the context of the submission, as the tests have already been conducted and the results are being reported.

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

This section is not applicable to a device like a surgical face mask that relies on objective, standardized physical and material property tests. The "ground truth" for these tests is defined by the ASTM, MIL, EN, and CFR standards themselves, which specify measurable criteria and methodologies. Expert interpretation in the traditional sense (e.g., radiologists reviewing images) is not involved in establishing the "ground truth" for physical performance or biocompatibility.

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

This is not applicable. Adjudication methods like 2+1 or 3+1 are typically used in studies involving human interpretation or clinical endpoints where there might be disagreement among evaluators of subjective data (e.g., medical image interpretation). For objective physical and material property tests, the outcome is determined by adherence to the standardized test procedure and measurement against defined thresholds.

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

This is not applicable. This device is a surgical face mask, not an AI-powered diagnostic tool. Therefore, an MRMC comparative effectiveness study involving human readers or AI assistance would not be conducted.

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

This is not applicable. This device is not an algorithm or an AI system. It is a physical medical device.

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

The "ground truth" for this device's performance is based on objective, standardized measurements against established criteria defined by international and national standards:

• ASTM F1862: Standard Test Method for Resistance of Medical Face Masks to Penetration by Synthetic Blood.
• ASTM F2101: Standard Test Method for Evaluating the Bacterial Filtration Efficiency (BFE) of Medical Face Mask Materials.
• ASTM F2299: Standard Test Method for Determining the Initial Efficiency of Materials Used in Medical Face Masks to Penetration by Particulates.
• MIL-M-36954C or EN 14683 Annex C: Standards for measuring differential pressure (breathing resistance).
• 16 CFR Part 1610: Standard for the flammability of clothing textiles.
• ISO 10993-5: Biological evaluation of medical devices – Part 5: Tests for in vitro cytotoxicity.
• ISO 10993-10: Biological evaluation of medical devices – Part 10: Tests for irritation and skin sensitization.

The "ground truth" is therefore the metrological result of these tests compared to the numerical thresholds specified in the respective standards.

8. The sample size for the training set

This is not applicable. This document describes the testing of a physical medical device (face mask), not an AI algorithm. Therefore, there is no "training set" in the machine learning sense. The tests are for product verification against established standards.

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

This is not applicable for the reasons stated in point 8.

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