AIMC Topic: Blastocyst
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Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification.
Reproductive biology and endocrinology : RB&E
Aug 8, 2024
PURPOSE: To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles.
Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos.
Chinese medical journal
Jul 12, 2024
BACKGROUND: In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous emb...
The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation.
Reproductive biology and endocrinology : RB&E
Jul 10, 2024
OBJECTIVE: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms.
Beyond black-box models: explainable AI for embryo ploidy prediction and patient-centric consultation.
Journal of assisted reproduction and genetics
Jul 4, 2024
PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.
Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction.
Reproductive biology and endocrinology : RB&E
May 22, 2024
BACKGROUND: The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analys...
Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model.
Scientific reports
May 8, 2024
Within the medical field of human assisted reproductive technology, a method for interpretable, non-invasive, and objective oocyte evaluation is lacking. To address this clinical gap, a workflow utilizing machine learning techniques has been develope...
A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.
Journal of ovarian research
Mar 15, 2024
BACKGROUND: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to...
Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss.
Reproductive biomedicine online
Mar 5, 2024
RESEARCH QUESTION: Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos?
An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes.
Reproductive biomedicine online
Jan 18, 2024
RESEARCH QUESTION: Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes?
The morphokinetic signature of human blastocysts with mosaicism and the clinical outcomes following transfer of embryos with low-level mosaicism.
Journal of ovarian research
Jan 9, 2024
BACKGROUND: Genetic mosaicism is commonly observed in human blastocysts. Embryos' morphokinetic feature observed from time-lapse monitoring (TLM) is helpful to predict the embryos' ploidy status in a non-invasive way. However, morphokinetic research ...
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