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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: © 2019 |Volume: 4 |Issue: 2 |Pages: 13
ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781522568612|DOI: 10.4018/IJBDAH.2019070102
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MLA

Tadepalli, Satya Kiranmai, and P.V. Lakshmi. "Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction." IJBDAH vol.4, no.2 2019: pp.21-33. http://doi.org/10.4018/IJBDAH.2019070102

APA

Tadepalli, S. K. & Lakshmi, P. (2019). Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 4(2), 21-33. http://doi.org/10.4018/IJBDAH.2019070102

Chicago

Tadepalli, Satya Kiranmai, and P.V. Lakshmi. "Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 4, no.2: 21-33. http://doi.org/10.4018/IJBDAH.2019070102

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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.