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Relieve symptoms of dry mouth, refresh, moisturize, soothes oral irritation, and lubricate oral dryness.

Salivea Dry Mouth Moisturizing Gel are specially formulated artificial saliva which contain moisturizers, humectants and salivary enzymes that collectively have lubricating, moisturizing, soothing and refreshing properties to relieve and treat the symptoms of dry mouth. The Salivea Dry Mouth Moisturizing Gel is supplied in 1 oz tube and sample foil pack.

Please note that the provided document is a 510(k) summary for a medical device (Salivea Dry Mouth Moisturizing Gel), which is a non-AI/ML device. Therefore, many of the requested criteria related to AI/ML model development and validation (e.g., test set data provenance, expert ground truth, MRMC studies, standalone performance, training set details) are not applicable to this type of submission.

The document primarily focuses on establishing substantial equivalence to predicate devices based on intended use, manufacturing, and non-clinical performance (biocompatibility, stability, physical properties).

Here's an analysis based on the information provided, highlighting what is applicable and what is not:

Acceptance Criteria and Study for Salivea Dry Mouth Moisturizing Gel

1. Table of Acceptance Criteria and Reported Device Performance

For this non-AI/ML device, "acceptance criteria" are typically related to physical properties, biocompatibility, and stability, rather than machine learning performance metrics. The document confirms that these criteria were met.

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

• Sample Size: Not explicitly stated in the context of a "test set" for performance evaluation in the way an AI/ML model would have one. For physical and biocompatibility testing, standard sample sizes according to relevant test methods would have been used but are not detailed here.
• Data Provenance: Not applicable in the context of a "test set" as understood for AI/ML. The data comes from the manufacturer's internal testing. The document does not specify the country of origin of the raw materials or testing facilities, nor does it refer to retrospective or prospective data collection from patients, as no clinical studies were performed.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Not Applicable. This is a non-AI/ML device. "Ground truth" in the AI/ML sense (e.g., expert annotations on images) is not relevant to its evaluation. The performance is assessed through biochemical and physical testing, and safety (biocompatibility) and stability studies, which rely on established laboratory standards and qualified technicians/scientists performing the tests.

4. Adjudication Method for the Test Set

• Not Applicable. No human "adjudication" of results in the AI/ML sense (e.g., resolving discrepancies in expert labels) was required. Test results are objective measurements from laboratory equipment or standardized biological assays.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No. An MRMC study is relevant for evaluating the impact of AI on human interpretation (e.g., radiologists reading images). Since this is a moisturizing gel and its evaluation did not involve human readers interpreting data, an MRMC study was not performed.

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

• Not Applicable. This is not an algorithm. Therefore, "standalone performance" of an AI algorithm is not relevant.

7. The Type of Ground Truth Used

• Not Applicable in the AI/ML sense. For this device, "ground truth" equates to:
  • Validated laboratory test methods: For pH, viscosity, specific gravity, and stability.
  • Standardized biocompatibility assays: Following ISO 10993 for irritation, sensitization, and toxicity.
  • Predicate device characteristics: The existing, legally marketed predicate devices serve as a benchmark for comparison regarding intended use, technological characteristics, and physiological purpose.

8. The Sample Size for the Training Set

• Not Applicable. This is a non-AI/ML device. There is no AI model, and therefore no "training set."

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

• Not Applicable. As there is no AI model or training set, "ground truth" establishment for a training set is not part of this submission.

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Relieves the symptoms of dry mouth, while moisturizing and lubricating oral dryness.

Moisyn is a specifically formulated artificial saliva substitute which contains moisturizers, humectants, and biopolymers that are designed to relieve dry mouth symptoms, soothe irritated oral surfaces, refresh, clean, and lubricate. The rinse is supplied as an 80z PET bottle or as a 20z PET spray bottle. Both bottles contain the same solution without any changes in formulation or intended use. Both the rinse and the Mist are formulated with and without spearmint flavor to provide the customer with a choice in flavoring.

The provided text is a 510(k) summary for the device "Moisyn Dry Mouth Oral Rinse/Mist". It describes the device, its intended use, comparison with predicate devices, and performance data. However, the information requested regarding acceptance criteria and a study proving the device meets these criteria (especially related to AI/algorithm performance, expert review, MRMC studies, etc.) is not present in this document.

This document is a regulatory submission for a medical device that relieves symptoms of dry mouth. It focuses on demonstrating substantial equivalence to existing predicate devices based on intended use, technological characteristics, and safety aspects (biocompatibility, preservation effectiveness, shelf-life, viral inactivation).

Here's a breakdown of why the requested information cannot be extracted:

• No AI/Algorithm Component: The device described is a physical oral rinse/mist, not an AI or algorithm-driven diagnostic or medical imaging device. Therefore, concepts like acceptance criteria for AI performance (sensitivity, specificity, AUC), test sets, training sets, ground truth establishment by experts, MRMC studies, or standalone algorithm performance are not applicable.
• Performance Data Focus: The "Performance data" section refers to standard biocompatibility testing, preservative effectiveness, and shelf-life, which are relevant for a chemical/physical product, not an AI.
• "Clinical Study" Context: The mention of a "Clinical Study" refers to a human study evaluating the effectiveness of the oral rinse in relieving dry mouth symptoms, not the performance of an AI or diagnostic algorithm. The study cited is about symptom improvement and saliva production, not diagnostic accuracy.

Therefore, I cannot provide a table of acceptance criteria for AI performance or details about an AI-related study because the device in question is not an AI/algorithm.

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