Ultrasound monitoring and hormonal blood testing are considered by many as an accurate method to predict ovulation time. However, uniform and validated algorithms for predicting ovulation have yet to be defined. Daily hormonal tests and transvaginal ultrasounds were recorded to develop an algorithm for ovulation prediction. The rupture of the leading ovarian follicle was a marker for ovulation day. The model was validated retrospectively on natural cycles frozen embryo transfer cycles with documented ovulation. Circulating levels of LH or its relative variation failed, by themselves, to reliably predict ovulation. Any decrease in estrogen was 100% associated with ovulation emergence the same day or the next day. Progesterone levels > 2 nmol/L had low specificity to predict ovulation the next day (62.7%), yet its sensitivity was high (91.5%). A model for ovulation prediction, combining the three hormone levels and ultrasound was created with an accuracy of 95% to 100% depending on the combination of the hormone levels. Model validation showed correct ovulation prediction in 97% of these cycles. We present an accurate ovulation prediction algorithm. The algorithm is simple and user-friendly so both reproductive endocrinologists and general practitioners can use it to benefit their patients.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10651856 | PMC |
http://dx.doi.org/10.1038/s41598-023-47241-2 | DOI Listing |
Hum Reprod
January 2025
Apple Inc. Health, Cupertino, CA, USA.
Study Question: Can algorithms using wrist temperature, available on compatible models of iPhone and Apple Watch, retrospectively estimate the day of ovulation and predict the next menses start day?
Summary Answer: Algorithms using wrist temperature can provide retrospective ovulation estimates and next menses start day predictions for individuals with typical or atypical cycle lengths.
What Is Known Already: Wrist skin temperature is affected by hormonal changes associated with the menstrual cycle and can be used to estimate the timing of cycle events.
Study Design, Size, Duration: We conducted a prospective cohort study of 262 menstruating females (899 menstrual cycles) aged 14 and older who logged their menses, performed urine LH testing to define day of ovulation, recorded daily basal body temperature (BBT), and collected overnight wrist temperature.
BMJ Open
December 2024
Faculdade de Medicina de São José do Rio Preto, São José do Rio Preto, Brazil.
Introduction: Infertility is a complex condition that affects millions worldwide, with significant physical, emotional and social implications. Mobile apps have emerged as potential tools to assist in the management of infertility by offering features such as menstrual cycle tracking, ovulation prediction, fertility education, lifestyle modification guidance and emotional support, thereby promoting reproductive health. Despite promising advancements such as the development of apps with sophisticated algorithms for ovulation prediction and comprehensive platforms offering integrated fertility education and emotional support, there remain gaps in the literature regarding the comprehensive evaluation of mobile apps for reproductive endocrinology and infertility.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Klick Applied Sciences, Klick Health, Toronto, ON, Canada.
Background: Identifying subtle changes in the menstrual cycle is crucial for effective fertility tracking and understanding reproductive health.
Objective: The aim of the study is to explore how fundamental frequency features vary between menstrual phases using daily voice recordings.
Methods: This study analyzed smartphone-collected voice recordings from 16 naturally cycling female participants, collected every day for 1 full menstrual cycle.
Front Endocrinol (Lausanne)
January 2025
Reproductive Medicine Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Objective: To build a prediction nomogram for early prediction of live birth probabilities according to number of oocytes retrieved in women ≤ 35 years of age.
Methods: A prediction model was built including 9265 infertile women ≤ 35 years of age accepting their first ovum pick-up cycle from January 2018 to December 2022. Least absolute shrinkage and selection operator (LASSO) regression was performed to identify independent predictors and establish a nomogram to predict reproductive outcomes.
Sci Rep
December 2024
Center for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
This study aimed to develop and validate a predictive model for failure to collect oocytes in the Patient-Oriented Strategies Encompassing Individualized Oocyte Number (POSEIDON) Groups 3 and 4 during their first in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycle. A retrospective analysis was conducted on patients in POSEIDON Groups 3 and 4 who underwent their first IVF/ICSI cycle at our center from January 2016 to December 2023. A total of 2,373 patients were randomly assigned to the training or validation cohort at a ratio of 6:4.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!