Search Results

Natural Cycles is a stand-alone software application, intended for women 18 years and older, to monitor their fertility. Natural Cycles can be used for preventing a pregnancy (contraception) or planning a pregnancy (conception).

Natural Cycles is an over-the-counter web and mobile-based standalone software application that monitors a woman's menstrual cycle using information entered by the user and informs the user about her past, current and future fertility status. The following information is used by the Natural Cycles software:
• daily temperature measurements
• information about the user's menstruation cycle (i.e., start date, number of days)
• optional ovulation or pregnancy test results.
A proprietary algorithm evaluates the data and returns the user's fertility status. Natural Cycles is available in three modes: Contraception (NC° Birth Control), Conception (NC° Plan Pregnancy), and Pregnancy (NC° Follow Pregnancy). For NC° Birth Control mode, the device provides predictions of "not fertile," shown as green days, and "use protection," shown as red days, that allow the user to determine the days on which her risk of conception is highest, and then make choices about either abstaining from sex or using a barrier method of contraception to prevent pregnancy. In addition to measuring daily basal body temperature with an oral thermometer with two decimal points, previous submissions cleared the Oura Ring and Apple Watch for automatic temperature input to the Natural Cycles algorithm. Natural Cycles can be used by women 18 years and older. Women who have been on hormonal birth control within 60 days prior to using Natural Cycles have a higher risk of becoming pregnant when compared to women who have not been on hormonal birth control within the 12 months prior to using the device. This device may not be right for women who have a medical condition where pregnancy would be associated with a significant risk to the mother or the fetus. The subject of this submission is the proposed inclusion of a Predetermined Change Control Plan (PCCP) that enables use of a broader range of temperature measuring devices for future wearable integrations with the Natural Cycles application. There are no changes to the Natural Cycles application from the application cleared in K231274.

The document describes the acceptance criteria and the results of a study for the Natural Cycles software application, which is intended for contraception and conception planning. This submission (K241006) primarily focuses on the inclusion of a Predetermined Change Control Plan (PCCP) to allow for the future integration of various temperature measuring devices, rather than changes to the core Natural Cycles application itself. The performance testing described is in the context of validating a new wearable temperature measuring device against the established performance of the Natural Cycles algorithm with existing temperature input methods (e.g., manual oral thermometer).

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance:

Note: The document states "The information outlined above demonstrates that Natural Cycles is as safe and effective as the predicate device," implying that the device (or rather, the combination of the Natural Cycles algorithm with any new wearable validated under the PCCP) meets these acceptance criteria.

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It refers to the KPIs in the context of validating new wearables. The study described is for evaluating the performance of the Natural Cycles algorithm when using temperatures from a device under consideration (i.e., a new wearable) as input.

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

The document does not specify the number or qualifications of experts used to establish ground truth for the test set.

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

The document does not specify any adjudication method for the test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, what was the effect size of how much human readers improve with AI vs without AI assistance:

This is a standalone software application for contraception/conception planning, primarily based on an algorithm processing temperature data. The concept of "human readers" or "AI assistance" in the context of medical image interpretation (which is where MRMC studies are common) is not applicable here. Therefore, no MRMC comparative effectiveness study was done, and there is no effect size for human readers improving with AI assistance.

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

Yes, the performance criteria (KPIs) listed are for the standalone algorithm's performance when analyzing temperature data, specifically within the context of a "device under consideration" (referring to a new wearable providing temperature input). The "Positive Percent Agreement (PPA)" refers to the algorithm's safety in predicting fertile windows without human intervention in the interpretation of the output days.

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

The ground truth appears to be established by comparing the algorithm's performance against biological markers and established menstrual cycle physiology, especially in relation to ovulation detection. For instance:

• "Detected ovulations – the fraction of cycles with positive LH where ovulation is detected by the Natural Cycles algorithm..." (LH refers to Luteinizing Hormone, a biological marker for ovulation).
• "Ovulation resolution – fraction of cycles with positive LH and ovulation detected, where ovulation is detected within two days from the LH-only ovulation day."
• The "Fraction of green days" and comparison to an "oral thermometer" suggest a benchmark against a standard measurement method and expected physiological outcomes.

8. The sample size for the training set:

The document does not specify the sample size for the training set.

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

The document does not explicitly describe how the ground truth for the training set was established. However, given the nature of the device, it can be inferred that the training would likely involve historical physiological data, including daily temperature readings, menstruation cycle information, and potentially LH/pregnancy test results, correlated with actual ovulation and fertility outcomes.

Ask a specific question about this device