Welcome to the InfoSci Platform
Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction

Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction

Satya Kiranmai Tadepalli, P.V. Lakshmi
Copyright: © 2022 |Pages: 12
ISBN13: 9781668462997|ISBN10: 1668462990|EISBN13: 9781668463000
DOI: 10.4018/978-1-6684-6299-7.ch029
Cite Chapter Cite Chapter

MLA

Tadepalli, Satya Kiranmai, and P.V. Lakshmi. "Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction." Research Anthology on Advancements in Women's Health and Reproductive Rights, edited by Information Resources Management Association, IGI Global, 2022, pp. 544-555. https://doi.org/10.4018/978-1-6684-6299-7.ch029

APA

Tadepalli, S. K. & Lakshmi, P. (2022). Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction. In I. Management Association (Ed.), Research Anthology on Advancements in Women's Health and Reproductive Rights (pp. 544-555). IGI Global. https://doi.org/10.4018/978-1-6684-6299-7.ch029

Chicago

Tadepalli, Satya Kiranmai, and P.V. Lakshmi. "Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction." In Research Anthology on Advancements in Women's Health and Reproductive Rights, edited by Information Resources Management Association, 544-555. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6299-7.ch029

Export Reference

Mendeley
Favorite

Abstract

Infertility is the combination of factors that prevent pregnancy. It involves a lot of care and expertise while selecting the best embryo to lead to a successful pregnancy. Assistive reproductive technology (ART) helps to solve this issue. In vitro fertilization (IVF) is one of the methods of ART which is very popular. Artificial intelligence will have digital revolution and manifold advances in the field of reproductive medicine and will eventually provide immense benefits to infertile patients. The main aim of this article is to focus on the methods that can predict the accuracy of pregnancy without human intervention. It provides successful studies conducted by using machine learning techniques. This easily enables doctors to understand the behavior of the attributes which are suitable for the treatment. Blastocyst images can be deployed for the detection and prediction of the best embryo which has the maximum chance of a successful pregnancy. This pioneering work gives one a view into how this field could benefit the future generation.