Real-world validation of a bleeding prediction algorithm in levonorgestrel intrauterine device users using the MyIUS mobile app

This study aimed to validate the real-world performance of the MyIUS mobile application algorithm in predicting bleeding intensity and regularity in levonorgestrel intrauterine device (LNG-IUD) 52 mg, 19.5 mg, and 13.5 mg users following device insertion. This was an observational real-world perform...

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Published in:	Contraception (Stoneham) Vol. 152; p. 111201
Main Authors:	Vieira, Carolina Sales, Eggebrecht, Lisa, Milhoranca, Igor Andre, Neumann, Robert, Acar, Mariana Schiffer, von Stockum, Sophia, Frenz, Ann-Kathrin
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.12.2025
Subjects:	Adolescent Adult Algorithm validation Algorithms Amenorrhea - chemically induced Artificial Intelligence Bleeding changes Bleeding prediction Contraceptive Agents, Female - administration & dosage Contraceptive Agents, Female - adverse effects Female Humans Intrauterine Devices, Medicated - adverse effects Levonorgestrel - administration & dosage Levonorgestrel - adverse effects Levonorgestrel intrauterine device Metrorrhagia Middle Aged Mobile Applications MyIUS app Prediction Algorithms Young Adult Algorithm validation Bleeding changes MyIUS app Bleeding prediction Artificial Intelligence Levonorgestrel intrauterine device
ISSN:	0010-7824, 1879-0518, 1879-0518
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Abstract	This study aimed to validate the real-world performance of the MyIUS mobile application algorithm in predicting bleeding intensity and regularity in levonorgestrel intrauterine device (LNG-IUD) 52 mg, 19.5 mg, and 13.5 mg users following device insertion. This was an observational real-world performance study conducted in Germany, Denmark, Sweden, Spain, Mexico, and Brazil, including women aged ≥ 18 years who provided electronic written informed consent and had used the MyIUS app for 90 days following LNG-IUD 52 mg, 19.5 mg, or 13.5 mg insertion. At day 90, participants received personalized bleeding predictions for one of three bleeding intensity clusters (predominantly amenorrhea, predominantly spotting, or predominantly bleeding), with those predicted predominantly spotting or predominantly bleeding receiving regular or irregular cycle predictions. After the 270-day data collection period, bleeding data self-reported from the last 180 days were evaluated and compared with the bleeding profile prediction. Based on 1734 participants with close-to-complete data sets, the overall multiclass area under the curve for the three LNG-IUDs was 0.81 (95% CI, 0.79–0.83) for bleeding intensity prediction, corresponding to sufficient discrimination in algorithm performance based on predefined thresholds. The overall area under the curve for menstrual cycle regularity prediction was 0.66 (95% CI, 0.63–0.68). Results validated the bleeding intensity prediction algorithm in a real-world environment, providing evidence to support the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion. Further refinement of the algorithm, especially regarding its performance in predicting cycle regularity, and testing of its clinical utility are warranted. This study validated the mobile application–housed bleeding prediction algorithm MyIUS in predicting bleeding intensity in LNG-IUD users following insertion in a real-world setting, supporting the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion.
AbstractList	This study aimed to validate the real-world performance of the MyIUS mobile application algorithm in predicting bleeding intensity and regularity in levonorgestrel intrauterine device (LNG-IUD) 52 mg, 19.5 mg, and 13.5 mg users following device insertion. This was an observational real-world performance study conducted in Germany, Denmark, Sweden, Spain, Mexico, and Brazil, including women aged ≥ 18 years who provided electronic written informed consent and had used the MyIUS app for 90 days following LNG-IUD 52 mg, 19.5 mg, or 13.5 mg insertion. At day 90, participants received personalized bleeding predictions for one of three bleeding intensity clusters (predominantly amenorrhea, predominantly spotting, or predominantly bleeding), with those predicted predominantly spotting or predominantly bleeding receiving regular or irregular cycle predictions. After the 270-day data collection period, bleeding data self-reported from the last 180 days were evaluated and compared with the bleeding profile prediction. Based on 1734 participants with close-to-complete data sets, the overall multiclass area under the curve for the three LNG-IUDs was 0.81 (95% CI, 0.79–0.83) for bleeding intensity prediction, corresponding to sufficient discrimination in algorithm performance based on predefined thresholds. The overall area under the curve for menstrual cycle regularity prediction was 0.66 (95% CI, 0.63–0.68). Results validated the bleeding intensity prediction algorithm in a real-world environment, providing evidence to support the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion. Further refinement of the algorithm, especially regarding its performance in predicting cycle regularity, and testing of its clinical utility are warranted. This study validated the mobile application–housed bleeding prediction algorithm MyIUS in predicting bleeding intensity in LNG-IUD users following insertion in a real-world setting, supporting the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion. This study aimed to validate the real-world performance of the MyIUS mobile-application algorithm in predicting bleeding intensity and regularity in levonorgestrel intrauterine device (LNG-IUD) 52 mg, 19.5 mg, and 13.5 mg users following device insertion.OBJECTIVEThis study aimed to validate the real-world performance of the MyIUS mobile-application algorithm in predicting bleeding intensity and regularity in levonorgestrel intrauterine device (LNG-IUD) 52 mg, 19.5 mg, and 13.5 mg users following device insertion.This was an observational real-world performance study conducted in Germany, Denmark, Sweden, Spain, Mexico, and Brazil, including women aged ≥18 who provided electronic written informed consent and had used the MyIUS app for 90 days following LNG-IUD 52 mg, 19.5 mg, or 13.5 mg insertion. At day 90, participants received personalized bleeding predictions for one of three bleeding intensity clusters (predominantly amenorrhea, predominantly spotting, or predominantly bleeding), with those predicted predominantly spotting or predominantly bleeding receiving regular or irregular cycle predictions. After the 270-day data collection period, bleeding data self-reported from the last 180 days were evaluated and compared with the bleeding profile prediction.STUDY DESIGNThis was an observational real-world performance study conducted in Germany, Denmark, Sweden, Spain, Mexico, and Brazil, including women aged ≥18 who provided electronic written informed consent and had used the MyIUS app for 90 days following LNG-IUD 52 mg, 19.5 mg, or 13.5 mg insertion. At day 90, participants received personalized bleeding predictions for one of three bleeding intensity clusters (predominantly amenorrhea, predominantly spotting, or predominantly bleeding), with those predicted predominantly spotting or predominantly bleeding receiving regular or irregular cycle predictions. After the 270-day data collection period, bleeding data self-reported from the last 180 days were evaluated and compared with the bleeding profile prediction.Based on 1,734 participants with close-to-complete datasets, the overall multiclass area under the curve (AUC) for the three LNG-IUDs was 0.81 (95% confidence interval [CI], 0.79-0.83) for bleeding intensity prediction, corresponding to sufficient discrimination in algorithm performance based on predefined thresholds. The overall AUC for menstrual cycle regularity prediction was 0.66 (95% CI, 0.63-0.68).RESULTSBased on 1,734 participants with close-to-complete datasets, the overall multiclass area under the curve (AUC) for the three LNG-IUDs was 0.81 (95% confidence interval [CI], 0.79-0.83) for bleeding intensity prediction, corresponding to sufficient discrimination in algorithm performance based on predefined thresholds. The overall AUC for menstrual cycle regularity prediction was 0.66 (95% CI, 0.63-0.68).Results validated the bleeding intensity prediction algorithm in a real-world environment, providing evidence to support the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion. Further refinement of the algorithm, especially regarding its performance in predicting cycle regularity, and testing of its clinical utility are warranted.CONCLUSIONResults validated the bleeding intensity prediction algorithm in a real-world environment, providing evidence to support the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion. Further refinement of the algorithm, especially regarding its performance in predicting cycle regularity, and testing of its clinical utility are warranted.This study validated the mobile application-housed bleeding prediction algorithm MyIUS in predicting bleeding intensity in levonorgestrel intrauterine device (LNG-IUD) users following insertion in a real-world setting, supporting the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion.IMPLICATIONSThis study validated the mobile application-housed bleeding prediction algorithm MyIUS in predicting bleeding intensity in levonorgestrel intrauterine device (LNG-IUD) users following insertion in a real-world setting, supporting the global use of MyIUS for individualized insights into bleeding changes following LNG-IUD insertion.
ArticleNumber	111201
Author	Eggebrecht, Lisa Milhoranca, Igor Andre von Stockum, Sophia Vieira, Carolina Sales Neumann, Robert Frenz, Ann-Kathrin Acar, Mariana Schiffer
Author_xml	– sequence: 1 givenname: Carolina Sales surname: Vieira fullname: Vieira, Carolina Sales email: carol.sales@usp.br organization: Department of Obstetrics and Gynecology, Ribeirao Preto Medical School, University of São Paulo, Brazil – sequence: 2 givenname: Lisa surname: Eggebrecht fullname: Eggebrecht, Lisa organization: ZEG Berlin, Center for Epidemiology and Health Research, Berlin, Germany – sequence: 3 givenname: Igor Andre surname: Milhoranca fullname: Milhoranca, Igor Andre organization: ZEG Berlin, Center for Epidemiology and Health Research, Berlin, Germany – sequence: 4 givenname: Robert surname: Neumann fullname: Neumann, Robert organization: Bayer Aktiengesellschaft, Berlin, Germany – sequence: 5 givenname: Mariana Schiffer surname: Acar fullname: Acar, Mariana Schiffer organization: Bayer Consumer Care AG, Basel, Switzerland – sequence: 6 givenname: Sophia surname: von Stockum fullname: von Stockum, Sophia organization: ZEG Berlin, Center for Epidemiology and Health Research, Berlin, Germany – sequence: 7 givenname: Ann-Kathrin surname: Frenz fullname: Frenz, Ann-Kathrin organization: Bayer Aktiengesellschaft, Wuppertal – Aprath, Germany
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Cites_doi	10.1186/s12905-022-01657-6 10.1186/s12978-023-01573-4 10.1016/j.fertnstert.2011.12.003 10.1016/j.contraception.2022.05.011 10.1080/13625187.2019.1630817 10.2147/IJWH.S99705 10.1016/S0010-7824(96)00189-8 10.1016/j.contraception.2019.05.017 10.12688/gatesopenres.13609.1 10.1097/AOG.0000000000000019 10.1080/13625187.2020.1843015 10.1016/j.contraception.2019.03.044 10.2196/24633 10.3109/13625187.2013.853034 10.1186/s12978-018-0561-0 10.1097/JTO.0b013e3181ec173d 10.1016/j.ajog.2014.07.025
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Keywords	Algorithm validation Bleeding changes MyIUS app Bleeding prediction Artificial Intelligence Levonorgestrel intrauterine device
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Snippet	This study aimed to validate the real-world performance of the MyIUS mobile application algorithm in predicting bleeding intensity and regularity in... This study aimed to validate the real-world performance of the MyIUS mobile-application algorithm in predicting bleeding intensity and regularity in...
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SubjectTerms	Adolescent Adult Algorithm validation Algorithms Amenorrhea - chemically induced Artificial Intelligence Bleeding changes Bleeding prediction Contraceptive Agents, Female - administration & dosage Contraceptive Agents, Female - adverse effects Female Humans Intrauterine Devices, Medicated - adverse effects Levonorgestrel - administration & dosage Levonorgestrel - adverse effects Levonorgestrel intrauterine device Metrorrhagia Middle Aged Mobile Applications MyIUS app Prediction Algorithms Young Adult
Title	Real-world validation of a bleeding prediction algorithm in levonorgestrel intrauterine device users using the MyIUS mobile app
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