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Femtex Gentle Touch tampons are menstrual tampons that are inserted into the vagina and used to absorb menstrual fluid.

Femtex Gentle Touch Tampons are menstrual tampons used to absorb menstrual fluid. Femtex Gentle Touch Tampons will be provided with 3 absorbencies, regular, super and super plus.

Femtex Gentle Touch Tampons are made from rayon and, cotton cord and sewing thread.

The material used in Femtex Gentle Touch tampons are similar to those used in other legally marketed tampons.

This 510(k) summary for K963034 describes a physical medical device (tampons), not an AI/ML powered device. As such, the requested information regarding acceptance criteria, study design for AI/ML performance, sample sizes for AI/ML models, expert involvement, and ground truth establishment for AI/ML is not applicable.

The summary focuses on demonstrating substantial equivalence to legally marketed tampons based on material composition, intended use, and non-clinical biocompatibility and absorbency testing.

Here's a breakdown of the specific points requested and why they are not relevant in this context:

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

• Not applicable for AI/ML. The document states "Assessment of Performance Standards: Not Applicable" in its own section. The non-clinical testing focused on safety (biocompatibility, irritation, sensitization) and absorbency. For absorbent devices like tampons, acceptance criteria would typically relate to absorption capacity, leakage, and material safety, not diagnostic or risk assessment performance metrics.

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

• Not applicable for AI/ML. This refers to a dataset for evaluating an algorithm. For a physical device, testing involves physical samples tested in a lab or in vivo. The document mentions "results of these tests" in a general way, but doesn't specify sample sizes for biocompatibility or absorbency tests, nor is there "data provenance" in the sense of patient data.

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 for AI/ML. Ground truth refers to the true label in an AI/ML context. For a physical device, testing results are typically objective measurements (e.g., absorbency in Syngyna testing, cellular response in irritation tests) based on established scientific protocols, not expert consensus on an image or medical record.

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

• Not applicable for AI/ML. Adjudication is a method to resolve discrepancies in expert labeling of a dataset for AI/ML. This is not relevant for testing a physical product like a tampon.

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 for AI/ML. MRMC studies are specifically for evaluating the impact of AI on human diagnostic performance. Tampons do not involve "human readers" or AI assistance in their use or evaluation.

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

• Not applicable for AI/ML. This refers to the performance of an AI algorithm on its own. Tampons are not an algorithm.

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

• Not applicable for AI/ML. As explained above, for a physical device, testing involves objective metrics rather than "ground truth" labels. For example, "0 irritation testing" implies a measurable outcome of no irritation, which is a direct observation/measurement, not a consensus interpretation.

8. The sample size for the training set

• Not applicable for AI/ML. This refers to data used to train an AI model. Tampons are not an AI model.

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

• Not applicable for AI/ML. This refers to labeling data for AI training. Tampons do not have a training set or ground truth in this context.

In summary, the provided 510(k) pertains to a traditional physical medical device and therefore the questions regarding AI/ML performance evaluation are not applicable. The device's safety and effectiveness are demonstrated through non-clinical biocompatibility and absorbency testing, which aligns with the regulatory pathway for such products.

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