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The PeriCoach® OTC 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.

The PeriCoach® OTC 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 contraction 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 describes the PeriCoach® OTC device. While it mentions the device meets acceptance criteria, it does not explicitly provide a table of acceptance criteria and reported device performance in the typical quantitative format for algorithmic performance metrics (e.g., sensitivity, specificity, AUC). Instead, the "acceptance criteria" are implied by the standards and bench tests it passed, and the "performance" is qualitative or relates to the device's physical and functional properties.

The document focuses on demonstrating substantial equivalence to a predicate device (PeriCoach® K143580) for a change from prescription use to over-the-counter (OTC) use, and software/firmware upgrades. Therefore, the "study" described is primarily focused on safety and equivalence, rather than a clinical trial proving specific diagnostic or treatment efficacy metrics typically found with AI/ML device submissions.

Here's an attempt to answer your questions based on the provided text, while acknowledging its limitations for an AI/ML context:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document doesn't provide a typical quantitative performance table for an AI/ML diagnostic. Instead, the acceptance criteria are met by demonstrating compliance with standards and successful bench testing. The "performance" is stated qualitatively in terms of safety, usability, and functional equivalence.

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

• Test Set Description: The document refers to a "post-market user survey." This survey appears to be the primary "test set" for assessing suitability for OTC use and general user experience.
• Sample Size: The exact sample size for the post-market user survey is not specified in the provided text. It is only referred to as "a post market user survey."
• Data Provenance: The document does not specify the country of origin for the survey data. It states the submission is from Australia ("Analytica Pty Ltd," "St Leonards, NSW Australia"), which might imply the survey was conducted there, but this is not explicitly stated. The survey is characterized as "post market," indicating it's retrospective relative to the device's initial market release (as a prescription device).

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

• Not Applicable in this context. The "ground truth" for this device's performance isn't established by expert review of medical images or data requiring clinical expertise (like radiologists for AI). Instead, the "ground truth" for the post-market user survey would be the users' direct self-reported experience and feedback, and for the bench testing, it was engineering measurements against pre-defined specifications. Health professionals might have been involved in designing the survey or interpreting aggregated results, but they were not "experts establishing ground truth" in the diagnostic AI sense.

4. Adjudication Method for the Test Set

• Not Applicable. Since the "test set" is a user survey and bench testing, there's no diagnostic decision or interpretation of complex medical data requiring an adjudication method like 2+1 or 3+1.

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

• No such study was performed or is relevant. This device is a biofeedback perineometer, not an AI-powered diagnostic imaging tool that assists human readers. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable to this submission.

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

• Partially Applicable. The "sensor behavior test" and the overall bench testing could be considered "standalone" technical performance assessments of the device's core functionality (measuring pelvic floor muscle strength). However, the primary "performance" assessment mentioned for the OTC transition is the user survey, which inherently involves human interaction with the device. There isn't an "algorithm only" performance measured in the sense of an AI model making a diagnosis/prediction without human input.

7. The Type of Ground Truth Used

• Engineering Specifications / User Experience Data.
  • For bench testing: The ground truth was defined by engineering specifications and relevant industry standards (e.g., specific thresholds for drop tests, durability cycles, immersion resistance, and sensor accuracy within tolerances).
  • For the user survey: The "ground truth" was derived from self-reported user feedback and experience data regarding ease of use, understanding, and safety concerns. There was no clinical outcome data (e.g., pathology or long-term therapeutic outcomes) presented to substantiate the device's efficacy, as its purpose is to aid in exercise, not diagnose a condition. The claim is for "strengthening of the pelvic floor muscles through exercise" with biofeedback, implying efficacy via adherence to exercise, not a direct treatment within the device itself.

8. The Sample Size for the Training Set

• Not applicable / Not specified for an AI model. The document describes a medical device, not an AI/ML algorithm that undergoes a training phase with a specific dataset. The device's "performance" is based on its physical and software design validation, and user experience.

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

• Not applicable. As above, this is not an AI/ML algorithm with a traditional "training set." The device's design and functionality would be based on engineering principles and potentially prior clinical knowledge about pelvic floor muscle training, not a machine learning training process.

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The PeriCoach 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.

The PeriCoach® 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 contraction 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 under prescription only.

The provided text appears to be a 510(k) summary for the PeriCoach device, which is a perineometer. This document is primarily focused on demonstrating "substantial equivalence" to a predicate device (Neen Healthcare Periform K002617) to obtain FDA clearance, rather than detailing a study that establishes explicit acceptance criteria and corresponding device performance in clinical use. Thus, much of the requested information regarding "acceptance criteria," "study that proves the device meets the acceptance criteria," "sample sizes," "expert ground truth," "adjudication methods," and "MRMC comparative effectiveness studies" is not explicitly present.

However, based on the information provided, we can infer some aspects related to non-clinical testing and the basis for substantial equivalence.

Here's an attempt to answer your questions based on the provided text, with many fields being marked as "Not explicit in text" where the information is absent:

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) submission, the "acceptance criteria" are implicitly met by demonstrating "substantial equivalence" to a legally marketed predicate device. The document summarizes the non-clinical testing performed to establish this. Explicit acceptance criteria with quantitative thresholds for performance metrics (e.g., sensitivity, specificity, accuracy) are not provided in this summary.

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

• Sample Size for Test Set: Not explicit in the text. The document refers to non-clinical testing (biocompatibility, electrical safety, mechanical tests, software V&V) of the device itself, rather than a clinical human subject test set.
• Data Provenance: Not explicit in the text. The company is based in Australia. The non-clinical tests were conducted according to international standards (ISO, IEC).
• Retrospective or Prospective: Not applicable as no clinical study on human subjects is described for proving performance against acceptance criteria in the context of efficacy for the indication. The tests were primarily device characterization.

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/Not explicit in the text. This document does not describe a clinical study where expert ground truth was established for a test set. The non-clinical tests rely on compliance with standards.

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

• Not applicable/Not explicit in the text. No clinical test set requiring adjudication is described.

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

• No MRMC comparative effectiveness study is mentioned. This device is a biofeedback perineometer, not an AI-assisted diagnostic tool for human readers.

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

• This is not an "algorithm only" device in the typical AI sense. Its primary function is a measurement and biofeedback device. The non-clinical testing establishes the standalone performance of the device's components (sensors, software, electrical safety, etc.) as detailed in Q1. The device itself (the probe and its communication to the smartphone app) constitutes the "standalone" element being tested for safety and basic function.

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

• For Non-Clinical Testing: The "ground truth" for the non-clinical tests referred to here is compliance with established international standards (ISO 10993, IEC 60601 series, IEC 62304) and manufacturer specifications for mechanical performance. There isn't a "clinical ground truth" like pathology or outcomes data presented in this summary for the purpose of demonstrating efficacy.

8. The sample size for the training set

• Not applicable/Not explicit in the text. This document does not pertain to an AI model that would require a "training set." The software verification and validation are for the device's operational software, not for training a predictive model.

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

• Not applicable/Not explicit in the text. Not relevant for this type of device and submission.

Summary Interpretation:

The provided document, a 510(k) summary, is intended to demonstrate that the PeriCoach device is "substantially equivalent" to a previously cleared predicate device. This is achieved by showing that:

1. It has the same intended use.
2. It has similar technological characteristics (or that any differences do not raise new questions of safety and efficacy).
3. Non-clinical testing (biocompatibility, electrical safety, mechanical durability, software V&V) confirms its safety and performance in alignment with relevant standards, thereby addressing any potential new safety or efficacy concerns arising from the differences (e.g., wireless feedback, different materials).

The document does not present results from a clinical trial or study designed to establish quantitative efficacy or performance metrics for the device's ability to "treat stress, mild-moderate urge and mixed urinary incontinence" in human subjects. The approval is based on the idea that if it's substantially equivalent in terms of safety and how it works, and the predicate device is already cleared for that indication, then this device can also be cleared.

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