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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 and prescription 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), and optional ovulation or pregnancy test results. A proprietary algorithm evaluates the data and returns the user's fertility status. Users can choose to use Natural Cycles in various modes based on their goals.

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 contraception to prevent pregnancy.

Inputs to the device include user-inputted daily basal body temperature measured with an oral thermometer with two decimal points or a compatible wearable device (e.g., Oura Ring and Apple Watch).

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 provided document is a 510(k) summary for the Natural Cycles device (K250561), which is a software application for contraception.

Based on the provided document, no study was conducted to prove the device meets acceptance criteria. The document explicitly states:

"There have been no changes to the technology of the device, and no new risks or increased likelihood or magnitude of identified risks have been introduced due to the change from over-the-counter to prescription and over-thecounter use. Therefore, no performance data is needed to validate this change."

This indicates that the submission relies on the substantial equivalence to a predicate device (K231274 Natural Cycles) which presumably demonstrated performance in its original submission. The current submission, K250551, is simply adding "Prescription Use" in addition to the existing "Over-The-Counter Use" without any technological changes to the device itself.

Therefore, most of the requested information regarding acceptance criteria and a study to prove the device meets them cannot be extracted from this document, as no such study was performed or needed for this specific 510(k) submission.

However, I can provide what is stated or implied regarding the device's characteristics and the rationale for the lack of new performance data.

Detailed Breakdown based on the provided document:

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

• Acceptance Criteria: Not explicitly stated as this submission is based on substantial equivalence and no new performance data was generated. The "acceptance criteria" for this submission are that the device remains "as safe and effective" as the predicate device despite the change in use classification.
• Reported Device Performance: No new performance data is reported for K250561. The document asserts that "The technology of the Natural Cycles Application is identical to the predicate device." Therefore, the performance is assumed to be equivalent to the predicate device (K231274).

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. No new test set data or studies were performed for this 510(k) submission (K250561).

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. No new test set and thus no experts were used for ground truth establishment for this specific submission.

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

• Not Applicable. No new test set and thus no adjudication method for ground truth establishment for this specific submission.

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. Natural Cycles is described as a "stand-alone software application" with a "proprietary algorithm" for fertility status. It is not an AI-assisted diagnostic tool that human "readers" would use to improve their performance. No MRMC study was conducted or is relevant to this device's function.

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

• Not Applicable. While Natural Cycles is described as a "stand-alone software application," this submission (K250561) does not report a new standalone performance study. It relies on the prior performance of the identical predicate device (K231274). The device's function inherently involves "human-in-the-loop" as users input data (temperature, menstruation info) and then interpret the device's output (red/green days) for contraception or conception planning. The algorithm's "standalone" performance would pertain to its accuracy in interpreting the input data to determine fertility status, which would have been established in the predicate device's submission.

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

• Not Applicable to this submission. The basis for fertility status determination (ground truth) for the predicate device's performance (upon which this submission relies) is not detailed here. For fertility tracking apps, this typically involves comparing the algorithm's predictions against biological indicators of ovulation (e.g., ultrasound, hormone levels) over multiple cycles, or analyzing real-world pregnancy rates for contraception claims.

8. The sample size for the training set

• Not Applicable to this submission. The document explicitly states "No changes have been made to the device technology," meaning no new training or re-training of the algorithm occurred for this submission (K250561). The training set data for the predicate device's algorithm is not disclosed in this document.

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

• Not Applicable to this submission. As noted above, no new training occurred. The method for establishing ground truth for the training set used for the predicate device's algorithm is not detailed in this document.

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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

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 body 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 (NCº Follow Pregnancy). For NCº 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, the predicate submission cleared the Oura Ring for automatic temperature input to the Natural Cycles algorithm, and the current submission expands to allow the device to utilize automatic temperature inputs from the Apple Watch.
  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 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.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary:

This device, Natural Cycles, is a software application for contraception. The 510(k) submission seeks to add the Apple Watch as an accepted source for daily basal body temperature (BBT) input, to be used by the existing Natural Cycles fertility algorithm. The core algorithm remains unchanged.

1. Table of Acceptance Criteria and Reported Device Performance

The core acceptance criterion for this submission revolves around the comparability of the algorithm's performance when using Apple Watch temperature data versus a traditional oral thermometer, specifically in the context of identifying ovulation and providing "green days" (not fertile). The study did not define explicit acceptance criteria in terms of specific sensitivity/specificity thresholds, but rather focused on demonstrating that the Apple Watch input did not negatively impact the safety and effectiveness as compared to the predicate device.

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

• Sample Size:
  • 272 users (de-identified information) met criteria for the main study.
  • A subset of 104 users was assessed for the ovulation Key Performance Indicator (KPI), requiring a positive luteinizing hormone (LH) test.
  • Data were collected over a total of 1918 menstrual cycles.
  • 505 of these cycles were considered complete and met study criteria.
  • 153 of the complete cycles had at least one positive LH test.
• Data Provenance:
  • Country of Origin: The majority (58%) of the women were located in the European Union (EU) and the United Kingdom (UK), with the remainder (42%) located in the US.
  • Retrospective or Prospective: Prospective. The study involved women actively wearing the Apple Watch nightly, recording temperatures, and providing LH test results over time.

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

This summary does not specify the number or qualifications of experts used. The ground truth for ovulation was established through Luteinizing Hormone (LH) test results. These are over-the-counter medical tests used by individuals to detect the LH surge that precedes ovulation, and their interpretation is generally self-reported by the user.

4. Adjudication Method for the Test Set

No explicit adjudication method is mentioned. The ground truth appears to be based on user-reported LH test results, which are biochemical markers rather than subjective expert interpretations requiring adjudication. The comparison was statistical, between the algorithm's output using Apple Watch data versus oral thermometer data, benchmarked against LH test results.

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

No, an MRMC study was not performed. This device is a standalone software application, and the study focused on the performance of the algorithm with different temperature inputs, not on human reader performance or improvement with AI assistance.

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

Yes, this was effectively a standalone algorithm performance evaluation. The study compared how the Natural Cycles algorithm performed in identifying ovulation and predicting "green days" when fed with temperatures from the Apple Watch versus an oral thermometer. The algorithm's output was then confirmed against LH test results. Humans were involved in providing the data (wearing the watch, taking oral temperature, performing LH tests) but not in interpreting images or making diagnoses that the AI would then assist with.

7. The Type of Ground Truth Used

The primary ground truth used for validating the ovulation detection performance was Luteinizing Hormone (LH) test results. This is a form of biochemical marker or outcomes-related data (in the sense that an LH surge is a biological event preceding ovulation).

8. The Sample Size for the Training Set

The document does not explicitly state the sample size for the training set. It refers to the Natural Cycles algorithm and states "There have been no changes to how the Natural Cycles algorithm determines the daily fertility status." This implies the algorithm was already trained and validated as part of prior 510(k) submissions (e.g., K202897, the predicate device). The current study is an evaluation of new input data rather than a re-training effort.

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

Since the training set details are not provided in this specific document (as the algorithm itself wasn't re-trained for this submission), this information cannot be definitively extracted from the provided text. However, given that Natural Cycles has been cleared for contraception previously, its algorithm would have been extensively validated with large datasets of menstrual cycle data, BBT readings, and confirmed ovulation events (likely through similar biochemical markers like LH, or possibly other clinical methods).

Ask a specific question about this device

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 body 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, and Pregnancy. For Contraception 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, the device allows automatic temperature input from the Oura ring, a wearable temperature monitor.
  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 preqnant when compared to women who have not been on hormonal birth control within the 12 months prior to using the 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.

Here's a breakdown of the acceptance criteria and study proving the device meets them, based on the provided FDA 510(k) summary for Natural Cycles (K202897):

1. Table of Acceptance Criteria and Reported Device Performance

The primary purpose of this 510(k) submission was to add the Oura ring as a new temperature input method for the Natural Cycles algorithm. Therefore, the acceptance criteria and performance data revolve around demonstrating that this new input method does not negatively impact the algorithm's contraceptive effectiveness.

• Cybersecurity information provided in accordance with the 2014 FDA guidance document ● Content of Premarket Submissions for Management of Cybersecurity in Medical Devices." (These directly state compliance with relevant software and cybersecurity guidelines.) |

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

• Sample Size for Test Set: 40 women.
• Data Provenance:
  • Country of Origin: The majority (38) of the women were located in Sweden, with one each in the US and Switzerland.
  • Retrospective or Prospective: Prospective. The study involved current data collection from participants wearing the Oura ring and using an oral thermometer over multiple menstrual cycles.

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

The document does not mention the use of human experts to establish ground truth for this specific clinical study. The ground truth for identifying ovulation was established by:

• Objective Physiological Markers: Luteinizing Hormone (LH) tests. Participants were asked to record the results of LH tests.
• Algorithmic Confirmation: The Natural Cycles algorithm's determination of ovulation.

The study aimed to compare the algorithm's performance with two different temperature inputs (oral thermometer vs. Oura ring) against an objective biological marker (LH tests).

4. Adjudication Method for the Test Set

No explicit adjudication method is described, as the ground truth was based on LH test results and the algorithm's determination, rather than subjective expert interpretations.

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

No, an MRMC comparative effectiveness study was not done. This study focused on comparing the performance of the algorithm with different data inputs (Oura ring vs. oral thermometer) in determining fertility status, using LH tests for validation. It did not involve multiple human readers assessing cases with and without AI assistance.

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

Yes, a standalone study was done. The study "was conducted to compare the daily temperatures generated by a wearable temperature monitor, the Oura ring, to a two decimal place oral thermometer for use by the NaturalCycles algorithm." The outcome measured was the algorithm's ability to identify ovulation and determine fertility status based on these inputs, and how the "green days" (not fertile) differed, without increasing the risk of unintended pregnancy. The human "in the loop" was the user providing the temperature data, but the performance being assessed was the algorithm's interpretation of that data.

7. The Type of Ground Truth Used

The primary ground truth used was:

• Luteinizing Hormone (LH) Test Results: Used to confirm ovulation occurrence (87 positive LH test results were submitted).
• Implied Clinical Outcome (Contraceptive Effectiveness): The statement "without increasing the risk of unintended pregnancy" refers to the overall clinical effectiveness as proven by prior studies of the Natural Cycles algorithm itself (likely referenced in the predicate device’s submission DEN170052), which this study aimed to maintain.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set for the Natural Cycles algorithm. It states, "There have been no changes to how the Natural Cycles algorithm determines the daily fertility status," implying the core algorithm was trained and validated prior to this submission (likely as part of the predicate device DEN170052). This submission focuses on validating a new input source for an existing, unchanged algorithm.

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

Since the document does not provide details on the training set, it does not specify how its ground truth was established. However, for a fertility tracking algorithm, the ground truth for training would typically involve:

• Large datasets of menstrual cycles: Including daily basal body temperature, menstruation dates, and confirmed ovulation dates.
• Confirmation of ovulation: Often through methods like ultrasound, blood hormone levels (e.g., progesterone for luteal phase confirmation), and LH tests.
• Pregnancy outcomes: For contraceptive effectiveness studies.

The detailed methods for the original algorithm's training and ground truth establishment would be found in the 510(k) submission for the predicate device, DEN170052. This current submission (K202897) is an update validating a new data input source for an already cleared and validated algorithm.

Ask a specific question about this device

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 entered into the application by the user:

• daily basal body temperature (BBT) 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.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

• Method Failure Rate: 0.6 per 100 women-years. This means 0.6 out of 100 women using the application for one year get pregnant due to the application incorrectly displaying a green day when the woman is fertile.
• Perfect Use Pearl Index: 1 per 100 women-years. This includes method failures and failures of a chosen contraceptive method on red days.
• Typical Use Pearl Index: 6.5 per 100 women-years (95% CI: 5.9-7.1). This accounts for all possible reasons for pregnancy, including user behavior (e.g., unprotected intercourse on red days, failure of contraceptive method used on red days, and method failure).

Subgroup Analysis (Typical Use PI):

• Recent Hormonal Birth Control use (within 60 days): 8.6 (7.2-10.0)
• No Hormonal Birth Control use (within 12 months): 5.0 (4.3-5.7)

The study enrolled 15,570 women for a total exposure of 7,353 woman-years. 475 pregnancies were observed (584 worst-case). The "Fraction of Days that were Green" was 48.8%. |
| 2. Human factors performance evaluation must be provided to demonstrate that the intended users can self-identify that they are in the intended use population and can correctly use the application, based solely on reading the directions for use for contraception. | A usability study was conducted with (b) (4) users. The study confirmed:

• 98.9% of users were within the intended age range (18-45).
• Analysis of sexual activity on red days: 29% of women had sex on red days. Of these, 49% used condoms, 25% withdrawal, 9% abstention, etc. Only 4% used no protection and took the risk.
• When asked why no protection was used on red days, responses showed that a high percentage understood the directive (e.g., trying to conceive, mistakenly confirming withdrawal, IUD in place, sex not penetrative). Only 2% stated they didn't know red meant fertile.
• Comparison of pregnancy rates between "Prevent Mode" (contraception) users and "Plan Mode" (conception) users demonstrated that users understand the labeling and behave accordingly (low pregnancy rate in Prevent Mode, high in Plan Mode).
• The study was conducted OUS but deemed generalizable to the US population due to similar education levels, user ages, temperature variation, and cycle lengths. |
  | 3. Software verification, validation, and hazard analysis must be performed... a. A cybersecurity vulnerability and management process... b. A description of the technical parameters of the software, including the algorithm... | Software Documentation: Major level of concern, with submitted documentation including Software/Firmware Description, Device Hazard Analysis, Software Requirement Specifications, Architecture Design Chart, Software Design Specifications, Traceability, Software Development Environment Description, Revision Level History, and Unresolved Anomalies.
• Risk Analysis: Comprehensive, addressing hazards, causes, severity, and control methods.
• Verification & Validation: Acceptable protocols for unit, integration, and system levels provided.
• Cybersecurity: Addressed data confidentiality, integrity, availability, DoS attacks, and malware with controls and evidence of performance.
• Technical Parameters/Algorithm: Full characterization provided, including description of the algorithm that analyzes BBT and menstrual cycle data to detect ovulation and determine fertility status. |
  | 4. Labeling must include specific warnings, instructions, and a summary of clinical validation. | Labeling via Instructions for Use manual (downloadable and in-app):
• Warnings/Precautions: Included (e.g., no contraceptive method is 100% effective, use another form of contraception on specified days, factors affecting accuracy, cannot protect against STIs).
• Hardware/OS Requirements: Not explicitly detailed in the provided text but implied as part of software documentation.
• Instructions: Identifies and explains how to use, including required user inputs and interpreting outputs.
• Clinical Summary: Provides a summary of the clinical validation study and results, including effectiveness and comparison to other methods. |

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

• Sample Size for Test Set: 15,570 women
• Data Provenance:
  • Country of Origin: 37 countries outside of the United States (OUS), with the majority from Sweden.
  • Retrospective or Prospective: Prospective. Women were followed prospectively from September 1, 2017, to April 30, 2018. A retrospective analysis was also conducted to validate ovulation identification.

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

The provided text does not specify the number of experts or their qualifications for establishing ground truth for the test set's clinical outcomes (pregnancies).

4. Adjudication method for the test set

The provided text does not specify an explicit adjudication method for the test set. Pregnancies were detected via "pregnancy tests, via email follow-up or via the algorithm." The "worst case" pregnancy count was calculated by assuming pregnancy in women who left early with unknown status or where data indicated possible pregnancy without confirmation. This suggests a blend of user reporting and algorithmic inference for pregnancy detection, but not a formal expert adjudication process for each case.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The study evaluates the device's standalone effectiveness as a contraceptive, which women use independently. It doesn't assess how human healthcare providers improve their diagnostic or decision-making ability with or without the AI's assistance.

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

Yes, a standalone study was done. The entire clinical study described (with the Pearl Index calculations) focuses on the Natural Cycles application's performance as a "stand-alone software application" for contraception. Women interact directly with the app, entering data, and the app provides fertility status. The effectiveness rates (method failure, perfect use, typical use Pearl Index) directly reflect the algorithm's performance in real-world use without a healthcare provider actively interpreting the algorithm's output for the user's daily contraceptive decisions.

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

The ground truth for the primary outcome (contraceptive effectiveness) was outcomes data: specifically, pregnancy detection. This was identified "via pregnancy tests, via email follow-up or via the algorithm."

For the retrospective analysis on ovulation identification: the ground truth was "ovulation day was correctly identified whether using temperature plus LH test results or just temperature alone." This implies comparison to either a gold standard of combined physiological markers (temperature + LH tests) or an internal standard from the algorithm itself for validation.

8. The sample size for the training set

The provided text does not explicitly state the sample size for the training set used to develop the Natural Cycles algorithm. It mentions that Natural Cycles "utilized real-world data to evaluate the effectiveness of the current version of the algorithm (v.3)," referring to the 15,570 women study as the evaluation of the algorithm, rather than its training.

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

The provided text does not explicitly describe how the ground truth for the training set was established. It states: "Natural Cycles has provided a full characterization of the technical parameters of the software, including a description of the algorithm that analyzes the patient's basal body temperature and menstrual cycle data to detect the day of ovulation and, by accounting for various sources of uncertainty, to determine the fertility status." This implies a biologically-based ground truth related to ovulation and fertile windows, likely established through extensive physiological research and potentially validated against various methods (e.g., hormone levels, ultrasound, BBT, LH tests) over time. However, the specific methodology for the training data ground truth is not detailed.

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