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510(k) Data Aggregation
(114 days)
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.
| Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|
| Algorithm's ability to identify ovulation with Apple Watch temperature data is comparable to oral thermometer data. | "The results of the clinical study demonstrated that when temperature was inputted from either the Apple Watch or the two-decimal place oral thermometer, the Natural Cycles algorithm was able to identify that ovulation had occurred, which was confirmed in the study by comparison to the 153 positive LH test results." |
| Use of Apple Watch temperature data does not increase the risk of unintended pregnancy (e.g., by incorrectly assigning "green days" in the fertile window). | "Compared to the two-decimal place oral thermometer, the Natural Cycles algorithm provided 0.6 fewer green days (not fertile) in the luteal phase of the menstrual cycle when the input temperature was from the Apple Watch without increasing the risk of unintended pregnancy." (Note: A reduction in "green days" in the luteal phase, post-ovulation, would generally be considered a more cautious or neutral outcome in terms of pregnancy risk, as the luteal phase, while generally infertile, should not be incorrectly identified as fertile.) |
| The change in temperature input source does not raise different questions of safety and effectiveness. | The summary concludes: "A comparison of intended use and technological characteristics combined with performance data demonstrates that Natural Cycles is as safe and effective as the predicate device and supports a determination of substantial equivalence." The 0.6 fewer green days with Apple Watch vs. 1.6 additional green days with Oura Ring (predicate) are noted, but explicitly stated: "however, this difference does not impact the safety and effectiveness of Natural Cycles for its intended use." This implicitly means the performance variations were within acceptable safety margins for contraceptive effectiveness. |
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).
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(268 days)
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.
| Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|
| Algorithm Performance Equivalence: The Natural Cycles algorithm, when using Oura ring temperature input, must maintain its ability to accurately identify ovulation and determine fertility status for contraception, comparable to using oral thermometer input, without increasing the risk of unintended pregnancy. | "the Natural Cycles algorithm was able to identify that ovulation had occurred, which was confirmed in the study by comparison to the 87 positive LH test results." (This indicates the core function of identifying ovulation was maintained). "Compared to the two-decimal place oral thermometer, the Natural Cycles algorithm provides additional 1.6 green days (not fertile) in the luteal phase of the menstrual cycle when the input temperature was from the Oura ring, without increasing the risk of unintended pregnancy." (This directly addresses the equivalence in contraceptive effectiveness by showing more "green days" without higher pregnancy risk, implying no decrease in safety/effectiveness). |
| Usability/Human Factors (Implicit for integration): The integration of Oura ring data should be seamless and not introduce new usability issues that compromise safety or effectiveness. (While not explicitly stated as a criterion, it's generally an underlying expectation for new input methods). | "Human factors testing in DEN170052 was used to support that device users could safely use the device." (This refers to prior human factors testing for the predicate device, implying that the general usability of Natural Cycles for temperature input was already established, and the Oura integration implicitly rode on this). The study design, with participants being "experienced users of both Natural Cycles and entering their daily temperature using an oral thermometer but did not have experience using the Oura ring with Natural Cycles," suggests that the focus was on the data integration, not on the fundamental usability of the app itself. |
| Software Compliance & Cybersecurity: The software handling Oura ring data must conform to regulatory standards. | "- Software documentation provided in accordance with the 2005 FDA guidance document Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices to support device software with a major level of concern. - 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.
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(324 days)
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
| Acceptance Criteria (Special Control) | Reported Device Performance |
|---|---|
| 1. Clinical performance testing must demonstrate the contraceptive effectiveness of the software in the intended use population.Specificity related to unintended pregnancy rate. | Clinical Study Results (v.3 algorithm, Sept 2017 - Apr 2018):- 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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