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The Perifit Care+ is a perineometer designed to treat stress, mild-moderate urge and mixed urinary incontinence in women, by strengthening of the pelvic floor muscles through exercise. This device provides biofeedback via smart phone technology.

Perifit Care+ is indicated for an adult female.

The Perifit Care+ device consists of a rigid probe covered in a silicone sheath that is temporarily inserted into the vagina. Sensors located under the sheath measure the strength of the user's pelvic floor muscles. This information is then transmitted wirelessly to a smartphone application in order to provide real-time feedback to the user. It is a single patient, reusable device to be supplied over-the-counter.

The provided document is a 510(k) summary for the Perifit Care+ device. It outlines the device's indications for use, technological characteristics, and comparison to a predicate device (Perifit K221476) to establish substantial equivalence.

Here's an analysis of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" in the format of specific thresholds for performance metrics. Instead, it relies on demonstrating substantial equivalence to a predicate device (Perifit K221476) through similarity in indications for use, technological characteristics, and safety testing.

The "reported device performance" is largely described through the completion of various non-clinical performance tests, indicating the device met the standards for safety and functionality, rather than achieving specific quantitative performance metrics against a defined acceptance criterion.

However, based on the provided text, we can infer some "acceptance criteria" from the testing performed and the comparison to the predicate.

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

The document does not specify exact sample sizes for each non-clinical performance test (e.g., number of units dropped, number of users for the questionnaire). It also does not explicitly state the provenance (country of origin, retrospective/prospective) for these tests, except that they were conducted to a standard (e.g., ISO, IEC). User testing via a questionnaire is mentioned, implying a human subject component, but details are absent.

This type of information is usually detailed in the full 510(k) submission, not typically in the public summary.

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

This information is not provided in the document. The studies described are primarily non-clinical, involving engineering and materials testing, and software validation, rather than clinical studies requiring expert ground truth for diagnostic accuracy. The "user testing through a questionnaire" is a form of user experience or usability testing, not typically a "ground truth" establishment in a diagnostic context.

4. Adjudication Method for the Test Set

Since there's no mention of a diagnostic accuracy study or establishment of "ground truth" by experts, an adjudication method (like 2+1 or 3+1) is not applicable and not mentioned in this summary.

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

No, a MRMC comparative effectiveness study was not done. The Perifit Care+ is a patient-operated perineometer providing biofeedback, not an AI-assisted diagnostic tool that would typically involve human readers interpreting output with or without AI assistance. The effectiveness is primarily linked to the patient's own exercise and biofeedback, not an external reader's interpretation.

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

The device is a perineometer that provides biofeedback for pelvic floor muscle exercise via a smartphone app. Its "performance" is based on its ability to accurately sense muscle contractions and transmit that data, along with the functionality of the app itself. The software verification and validation (and "sensor behavior testing") would assess its standalone performance in these aspects. There's no "algorithm only" performance reported in the sense of an AI model making a diagnosis or prediction without human input, as this is not the nature of the device.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the non-clinical tests, the "ground truth" would be established by:

• Engineering specifications and standards: For mechanical, electrical, and sensor performance (e.g., a drop test either meets or fails a predetermined physical integrity standard).
• Reference materials/methods: For biocompatibility testing (e.g., cell cultures, animal models for irritation).
• Software requirements and design specifications: For software V&V (the software performs as designed and meets requirements).
• User feedback/comprehension: For the user questionnaire (users correctly understand labeling and operation).

There is no mention of ground truth established by expert consensus, pathology, or outcomes data related to diagnostic accuracy, as it's not a diagnostic device.

8. The Sample Size for the Training Set

This information is not provided and is generally not applicable in this context. The device is not an AI/ML model that is "trained" on a dataset in the conventional sense for making diagnostic predictions. Its software includes an application and embedded firmware, which are developed and validated against requirements, not "trained."

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

As there's no mention of a "training set" in the context of machine learning, this information is not applicable and not provided. Software "ground truth" would be established by its adherence to design specifications and user requirements during the development and validation phases.

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