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The Intimate Rose Vaginal Dilators are tools intended for controlled dilation of the vagina. They can be used for dilation for an examination (by your doctor), in preparation for a surgical procedure, or to help relieve the symptoms of vaginismus (condition that involves tightening of the vaginal muscles) and related painful sex.

The Intimate Rose Vaginal Dilators are intended to treat women suffering from vaginismus (including related dyspareunia). Vaginismus is the involuntary spasm of the muscles in the vaginal wall which then inhibits sexual intercourse by making it painful or impossible. Dyspareunia is the pain experienced during sexual intercourse caused by physical and/or emotional problems. The device is used as a tool to dilate the vagina in controlled stages. It is a reusable device that comes into contact with the vaginal mucosal membrane for a prolonged (> 24 hours to

The provided text describes the regulatory clearance (510(k)) for the Intimate Rose Vaginal Dilators, not a study proving device performance against acceptance criteria in the typical sense of a clinical trial for a novel AI/software medical device.

Therefore, many of the requested sections are not applicable to the information contained in the provided FDA 510(k) summary. The document focuses on demonstrating substantial equivalence to a previously cleared predicate device, rather than providing raw performance data against specific acceptance criteria for a new device's functionality.

However, I can extract the relevant information from the document as follows:

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

This information is not provided in a performance-against-acceptance-criteria format. The submission relies on demonstrating substantial equivalence to predicate and reference devices, aligning indications for use, and leveraging a previous self-selection study. There are no quantitative performance metrics (e.g., sensitivity, specificity, accuracy) mentioned, as this is a physical medical device, not an AI or diagnostic software.

The "performance" described is the device's ability to fulfill its intended use and be safe and effective, which is primarily assessed through comparison to existing devices and appropriate labeling.

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

The document mentions leveraging a "self-selection study conducted for the predicate device."

• Sample Size: Not specified for the predicate device's self-selection study.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It notes that the study was "previously conducted."

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

Not applicable. The self-selection study for the predicate device likely assessed lay users' ability to understand labeling and determine if the device was appropriate for them, rather than requiring expert ground truth for medical outcomes.

4. Adjudication method for the test set

Not applicable. No expert adjudication process is described for the self-selection study.

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 a physical vaginal dilator, not an AI/software medical device requiring MRMC studies.

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

Not applicable. This is a physical device, not an algorithm.

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

The document refers to a "self-selection study." The "ground truth" for such a study would likely be the user's ability to correctly interpret the labeling and self-assess their indication for use, as determined by the study design and evaluation criteria. It is not expert consensus, pathology, or outcomes data in a direct medical sense for this type of device.

8. The sample size for the training set

Not applicable. This is a physical device; there is no "training set" in the context of an AI/ML algorithm.

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

Not applicable. There is no training set for this type of device.

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