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Vagisan MoistCream Cremolum is a personal lubricant, a suppository for vaginal application, intended to moisturize and lubricate, to enhance the ease and comfort of intimate sexual activity and supplement the body's natural lubrication. This product is not compatible with natural rubber latex condoms and synthetic (polyurethane and polyisoprene) condoms.

The Vagisan MoistCream Cremolum is a ready-to-use, non-sterile, glycerin-based personal lubricant suppository for intravaginal application that melts after digital insertion to form a smooth cream. The device is intended to moisturize and lubricate, to enhance the ease and comfort of intimate sexual activity and supplement the body's natural lubrication. Vagisan MoistCream Cremolum consists of hydrogenated coco-glycerides, ceteary1 alcohol, lactic acid, calcium lactate, PEG-20 glyceryl stearate, and sodium carbomer. Each vaginal suppository is individually packaged in a foil blister pack, which are sold in boxes of eight or 16 vaginal suppositories.

This document is a 510(k) Summary for a medical device called "Vagisan MoistCream Cremolum." It is a premarket notification to the FDA to demonstrate substantial equivalence to a predicate device. The information provided does not describe a study involving an AI/Machine Learning device. Instead, it describes non-clinical performance testing for a personal lubricant.

Therefore, many of the requested categories (such as sample sizes for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC studies, standalone algorithm performance, and ground truth establishment for AI/ML) are not applicable to this document.

However, I can extract the acceptance criteria and reported device performance from the provided "Device Specifications" table and summarize the non-clinical performance tests that were conducted.

Acceptance Criteria and Device Performance for Vagisan MoistCream Cremolum

1. Table of Acceptance Criteria and Reported Device Performance

Study Details:

This document describes non-clinical performance testing for a personal lubricant, not a study evaluating an AI/Machine Learning device. Therefore, the following AI/ML specific information is largely not applicable.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: Not applicable. This document details non-clinical laboratory testing of a physical product, not a data-driven AI/ML model. The "test set" would refer to the batches of product samples used in each specific test (e.g., a certain number of suppositories for mass uniformity, a certain number of samples for microbial count), but specific sample sizes for each test are not provided in this summary.
• Data Provenance: Not specified for individual tests. The manufacturer is based in Germany (Dr. August Wolff GmbH & Co. KG Arzneimittel, address D-33611 Bielefeld, Germany). The testing standards cited are international (ISO, Ph. Eur., USP) suggesting internationally recognized lab practices. The submission is to the U.S. FDA.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable. The "ground truth" for the physical and chemical properties (like pH, osmolality, mass, microbial limits) are established by standard analytical and microbiological testing methods, not by expert consensus as would be for clinical image interpretation. Biocompatibility testing results are also based on standardized assays.

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

• Not applicable. Adjudication methods like 2+1 are used for expert consensus in fields like clinical image interpretation, not for objective measurements of physical or chemical properties.

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 not an AI device.

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

• Not applicable. This is not an AI device.

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

• The ground truth for the device's performance relies on objective laboratory measurements and standardized test procedures as defined by pharmacopeial monographs (Ph. Eur., USP) and ISO standards for biocompatibility.

8. The sample size for the training set

• Not applicable. This is not an AI device.

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

• Not applicable. This is not an AI device.

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