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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:0010-7824
1879-0518
1879-0518
DOI:10.1016/j.contraception.2025.111201