Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective-Reference-Cited by-同舟云学术

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Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective

Published:2019-02 Issue:2 Volume:111 Page:318-326
ISSN:0015-0282
Container-title:Fertility and Sterility
language:en
Short-container-title:Fertility and Sterility

Author:

Blank Celine^ORCID,Wildeboer Rogier Rudolf,DeCroo Ilse,Tilleman Kelly,Weyers Basiel^ORCID,de Sutter Petra,Mischi Massimo,Schoot Benedictus Christiaan

Funder

Dutch Technology Foundation TTW

Applied Science Division of The Netherlands Organization for Scientific Research

Technology Program of the Ministry of Economic Affairs

Publisher

Elsevier BV

Subject

Obstetrics and Gynecology,Reproductive Medicine

Reference37 articles.

1. A prediction model for live birth and multiple births within the first three cycles of assisted reproductive technology;Luke;Fertil Steril,2014

2. Personalized prediction of first-cycle in vitro fertilization success;Choi;Fertil Steril,2013

3. Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers;Vaegter;Fertil Steril,2017

4. Individualized decision-making in IVF: calculating the chances of pregnancy;van Loendersloot;Hum Reprod,2013

5. Can differences in IVF success rates between centres be explained by patient characteristics and sample size?;Lintsen;Hum Reprod,2010

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1. Decoupling Implantation Prediction and Embryo Ranking in Machine Learning: The Impact of Clinical Data and Discarded Embryos;Advanced Intelligent Systems;2024-09-02

2. Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications;Mayo Clinic Proceedings: Digital Health;2024-08

3. Advanced KPI Framework for IVF Pregnancy Prediction Models in IVF protocols;2024-06-04

4. Development of a machine learning–based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population;Journal of Assisted Reproduction and Genetics;2024-05-31

5. Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study;Reproductive Biology and Endocrinology;2024-05-22

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