Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF

2021 ◽  
Author(s):  
Claudio Michael Louis ◽  
Alva Erwin ◽  
Nining Handayani ◽  
Arie A. Polim ◽  
Arief Boediono ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
In Vitro Fertilization ◽  
Vitro Fertilization ◽  
The World ◽  
Computer Vision Technology ◽  
Vision Application

scholarly journals Genetic Gestational Surrogacy: Hope for Muslims

2018 ◽  
Vol 8 (3) ◽  
pp. 1-8
Author(s):  
Shamima Parvin Lasker ◽  
Marcello Ghilardi
Keyword(s):  
In Vitro Fertilization ◽  
Major Part ◽  
Muslim World ◽  
Surrogate Mother ◽  
Islamic Theology ◽  
Vitro Fertilization ◽  
Legal Procedure ◽  
The World ◽  
Gestational Surrogacy

More than half a million couples may be suffering from infertility in the world. When in vitro fertilization is unsuccessful, surrogacy may be a substitute choice for many couples. Literature shows that ten million Muslims are infertile worldwide. According to Islamic theology the concept of surrogacy is null and void as formation of blastocyst constitutes from sperm that is transferred to the uterus of a woman who is not married to him. In Islam, marriage is the only legal procedure to procreation for preservation of lineage, inheritance, prevention of adultery and prevention of possibility of incest among the half-siblings.  Genetic gestational surrogacy (sperm of husband and ovum of wife is fertilized by IVF procedure and transfer the embryos to the surrogate mother) may be free from social, legal and moral complications. Some Islamic countries have reluctant law in favour of surrogacy, as for example Iran, Lebanon and sporadic parts of the Muslim world. This article has attempted to find out a valid notion for accepting genetic gestational surrogacy in major part of the Muslim world that may reduce the peril of women who can not give a birth baby.

scholarly journals Machine learning predicts live-birth occurrence before in-vitro fertilization treatment

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ashish Goyal ◽  
Maheshwar Kuchana ◽  
Kameswari Prasada Rao Ayyagari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Feature Selection ◽  
Deep Learning ◽  
In Vitro Fertilization ◽  
Live Birth ◽  
Egg Quality ◽  
Roc Curves ◽  
Vitro Fertilization

AbstractIn-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to penetrate or survive in the cervical mucus and low sperm counts, resulting human infertility. Nevertheless, IVF does not guarantee success in the fertilization. Choosing IVF is burdensome for the reason of high cost and uncertainty in the result. As the complications and fertilization factors are numerous in the IVF process, it is a cumbersome task for fertility doctors to give an accurate prediction of a successful birth. Artificial Intelligence (AI) has been employed in this study for predicting the live-birth occurrence. This work mainly focuses on making predictions of live-birth occurrence when an embryo forms from a couple and not a donor. Here, we compare various AI algorithms, including both classical Machine Learning, deep learning architecture, and an ensemble of algorithms on the publicly available dataset provided by Human Fertilisation and Embryology Authority (HFEA). Insights on data and metrics such as confusion matrices, F1-score, precision, recall, receiver operating characteristic (ROC) curves are demonstrated in the subsequent sections. The training process has two settings Without feature selection and With feature selection to train classifier models. Machine Learning, Deep learning, ensemble models classification paradigms have been trained in both settings. The Random Forest model achieves the highest F1-score of 76.49% in without feature selection setting. For the same model, the precision, recall, and area under the ROC Curve (ROC AUC) scores are 77%, 76%, and 84.60%, respectively. The success of the pregnancy depends on both male and female traits and living conditions. This study predicts a successful pregnancy through the clinically relevant parameters in In-vitro fertilization. Thus artificial intelligence plays a promising role in decision making process to support the diagnosis, prognosis, treatment etc.

In vitro fertilization (IVF) and the risk of birth and developmental defects - facts and fictions

2009 ◽  
Vol 40 (3) ◽  
pp. 145-155
Author(s):  
Barbara Dolinska
Keyword(s):  
In Vitro Fertilization ◽  
Health Condition ◽  
Research Data ◽  
Advanced Age ◽  
Developmental Defects ◽  
Vitro Fertilization ◽  
The World ◽  
Natural Way

In vitro fertilization (IVF) and the risk of birth and developmental defects - facts and fictions Poland is being swept by a wave of discussions on various aspects of IVF application. Scientists of various disciplines are getting involved in these discussions as opponents to this form of procreation. Referring to research carried out all over the world, they demonstrate that children born thanks to the in vitro procedure are significantly more susceptible to all sorts of disease. The author, surveying available research data, shows that, in reality, the worse health of in vitro-conceived children deals with a narrow number of well-identified disorders and in most cases is of correlative, not causative nature. The main reason for the weaker health of these children is often connected with the advanced age of the parents who choose IVF and their health condition (mothers' in particular), as compared to those who become parents in a natural way.

scholarly journals Optomechatronic System For Automated Intra Cytoplasmic Sperm Injection

2015 ◽  
Vol 45 (4) ◽  
pp. 3-14
Author(s):  
Assen Shulev ◽  
Tihomir Tiankov ◽  
Detelina Ignatova ◽  
Kostadin Kostadinov ◽  
Ilia Roussev ◽  
...  
Keyword(s):  
Computer Vision ◽  
In Vitro Fertilization ◽  
Vitro Fertilization ◽  
Complete Automation ◽  
Intra Cytoplasmic Sperm Injection

Abstract This paper presents a complex optomechatronic system for In-Vitro Fertilization (IVF), offering almost complete automation of the Intra Cytoplasmic Sperm Injection (ICSI) procedure. The compound parts and sub-systems, as well as some of the computer vision algorithms, are described below. System capabilities for ICSI have been demonstrated on infertile oocyte cells.

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