K Number
K163029
Date Cleared
2017-06-19

(231 days)

Product Code
Regulation Number
N/A
Panel
DE
Reference & Predicate Devices
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

Relieves the symptoms and discomfort of dry mouth, refresh, moisturize/hydrate, clean, soothe oral irritation and lubricate oral dryness.

Device Description

Hydris™Oral Rinse(Hydris) is a specially formulated water soluble artificial saliva substitute with a pH between 5.00 to 7.00 for use at home in the oral cavity. The proposed device is formulated with water, humectants/moisturizers, including two moisture-rich humectants that are plant based, thickeners/binders, buffers, sweeteners, flavor, and colorant that collectively have moisturizing/hydrating, soothing, and refreshing properties. The device is provided ready to use as a non-sterile, semi-viscous blue colored liquid packaged in three sizes, including 8.5 FL OZ, 16.9 FL OZ and 33.8 FL OZ white polyethylene terephthalate (PET) bottles with white polypropylene caps. The shelf-life is 2 years.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study information for Hydris™ Oral Rinse based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the comparison to the predicate device (Biotene® Dry Mouth Oral Rinse). The objective was to demonstrate that Hydris™ Oral Rinse is "not significantly different" in key performance characteristics.

Acceptance Criteria (Implicit from Predicate Comparison)Reported Device Performance (Hydris™ Oral Rinse)
Not significantly different in ability to retain moisture/hydrationPass / Not statistically different from Biotene® Dry Mouth Oral Rinse
Not significantly different in moisturization/hydration of soft tissue at a specific time and temperaturePass / Not statistically different from Biotene® Dry Mouth Oral Rinse

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

The document does not explicitly state the sample sizes used for the bench testing.
The data provenance is also not explicitly stated in terms of country of origin or whether it was retrospective or prospective. It is described as "Nonclinical Testing" and "Bench testing," implying laboratory-based testing rather than clinical trials with human subjects.

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

This information is not provided in the document. For this type of bench testing, "ground truth" would typically refer to established scientific methods and instruments used for measurement, rather than expert consensus on medical cases.

4. Adjudication Method for the Test Set

This information is not applicable as the evaluations were bench tests comparing physical properties, not interpretations of medical data 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

An MRMC study was not done. This document describes the clearance of an oral rinse, not an AI-assisted diagnostic device.

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 an oral rinse, not an algorithm or AI system. The performance testing was standalone in the sense that it was bench testing of the product itself.

7. The Type of Ground Truth Used

The "ground truth" for the performance evaluation was established through bench testing results using scientific measurements to assess properties like moisture retention and moisturization/hydration. The predicate device (Biotene® Dry Mouth Oral Rinse) served as the comparative standard.

8. The Sample Size for the Training Set

This document does not describe the development of a machine learning algorithm, so there is no training set in this context.

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

This question is not applicable as there was no training set for a machine learning model.

N/A