4.6 Article

An interpretable machine learning model for individualized gonadotrophin starting dose selection during ovarian stimulation

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Obstetrics & Gynecology

An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation

Michael Fanton et al.

Summary: This study developed an interpretable machine learning model to optimize the day of trigger in in vitro fertilization cycles. The model used linear regression with follicle counts and estradiol levels to predict early and late triggers, which were found to result in fewer mature oocytes, fertilized oocytes, and usable blastocysts.

FERTILITY AND STERILITY (2022)

Article Obstetrics & Gynecology

General infertility workup in times of high assisted reproductive technology efficacy

Nikolaos P. Polyzos et al.

Summary: The evaluation of oocyte quality and quantity, as well as endocrine profile, is crucial in the assessment of infertility in couples. Recent advancements in clinical and genetic biomarkers have greatly improved the understanding and management of ovarian function and endocrine disorders in women undergoing assisted reproductive technology. This review provides an overview of current and novel strategies for assessing ovarian function and identifying endocrine disorders, as well as the role of genetic biomarkers and expanded carrier screening in the preliminary workup of infertile women.

FERTILITY AND STERILITY (2022)

Article Genetics & Heredity

FSH dose is negatively correlated with number of oocytes retrieved: analysis of a data set with ∼650,000 ART cycles that previously identified an inverse relationship between FSH dose and live birth rate

Zaramasina L. Clark et al.

Summary: The study aimed to evaluate the correlation between total FSH dose and number of retrieved oocytes, finding a negative relationship between the two. Regardless of patient health status, age, BMI, ovarian stimulation protocol, or infertility diagnosis, the optimal FSH dose range was between 1001-2000 IU, resulting in the highest number of retrieved oocytes.

JOURNAL OF ASSISTED REPRODUCTION AND GENETICS (2021)

Article Obstetrics & Gynecology

A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes

Eduardo Hariton et al.

Summary: This study utilized a machine learning causal inference model to optimize trigger injection timing in IVF patients, resulting in a significant increase in 2PNs and total usable blastocysts compared to physician decisions. The algorithm-assisted trigger decisions yielded on average more 2PNs and usable blastocysts per stimulation.

FERTILITY AND STERILITY (2021)

Review Developmental Biology

Artificial intelligence in reproductive medicine

Renjie Wang et al.

REPRODUCTION (2019)

Article Endocrinology & Metabolism

Discordance between antral follicle counts and anti-Mullerian hormone levels in women undergoing in vitro fertilization

Yangyang Zhang et al.

REPRODUCTIVE BIOLOGY AND ENDOCRINOLOGY (2019)

Article Obstetrics & Gynecology

The Impact of Maternal Body Mass Index on In Vitro Fertilization Outcomes

Alexandra Legge et al.

JOURNAL OF OBSTETRICS AND GYNAECOLOGY CANADA (2014)

Article Obstetrics & Gynecology

Individualizing FSH dose for assisted reproduction using a novel algorithm: the CONSORT study

F. Olivennes et al.

REPRODUCTIVE BIOMEDICINE ONLINE (2011)

Article Obstetrics & Gynecology

What is the optimum maximal gonadotropin dosage used in microdose flare-up cycles in poor responders?

Murat Berkkanoglu et al.

FERTILITY AND STERILITY (2010)

Article Obstetrics & Gynecology

Costs and outcomes associated with IVF using recombinant FSH

W. Ledger et al.

REPRODUCTIVE BIOMEDICINE ONLINE (2009)