Authors :
Hrishikesh Rajulu; Charan Reddy; Naveen Pendem; TarizAtique; Dr. Shwetha Buchanalli; Bharani Kumar Depuru
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/2p9t439u
Scribd :
https://tinyurl.com/y9drrxpt
DOI :
https://doi.org/10.5281/zenodo.10353687
Abstract :
Fertility treatments, particularly in the
context of in vitro fertilization (IVF), have seen
significant advancements in recent years, revolutionizing
the prospects for couples facing infertility. The quality of
the embryo is a critical factor influencing the success of
these treatments. Traditional methods for embryo
assessment have limitations in accuracy and efficiency,
prompting the need for innovative techniques. This
research study explores the application of deep learning
models to enhance embryo quality assessment in the field
of reproductive medicine.
The study involves the collection of a large dataset
of embryo images, Leveraging state-of-the-art deep
learning algorithms for the automated evaluation of
embryo quality. The deep learning models can
accurately predict embryo quality and developmental
potential, offering a valuable tool for clinicians and
embryologists.
The outcomes showcase the exceptional
performance of the deep learning models, markedly
enhancing both the speed and accuracy of embryo
quality assessment. This advancement not only enhances
the efficiency of fertility treatments but also contributes
to better patient outcomes by facilitating the selection of
the most viable embryos for fertilization.
The findings of this research have the potential to
transform the field of reproductive medicine, making
fertility treatments more accessible, cost-effective, and
successful. Utilizing the capabilities of deep learning, this
research marks a hopeful stride in enhancing the
prospects of conception for individuals and couples
grappling with infertility.
Keywords :
Fertility treatments, In vitro fertilization (IVF), Embryo quality, Embryo assessment, Convolutional Neural Network (CNN), Deep learning, Reproductive medicine, Automated assessment, Embryo viability, Reproductive health.
Fertility treatments, particularly in the
context of in vitro fertilization (IVF), have seen
significant advancements in recent years, revolutionizing
the prospects for couples facing infertility. The quality of
the embryo is a critical factor influencing the success of
these treatments. Traditional methods for embryo
assessment have limitations in accuracy and efficiency,
prompting the need for innovative techniques. This
research study explores the application of deep learning
models to enhance embryo quality assessment in the field
of reproductive medicine.
The study involves the collection of a large dataset
of embryo images, Leveraging state-of-the-art deep
learning algorithms for the automated evaluation of
embryo quality. The deep learning models can
accurately predict embryo quality and developmental
potential, offering a valuable tool for clinicians and
embryologists.
The outcomes showcase the exceptional
performance of the deep learning models, markedly
enhancing both the speed and accuracy of embryo
quality assessment. This advancement not only enhances
the efficiency of fertility treatments but also contributes
to better patient outcomes by facilitating the selection of
the most viable embryos for fertilization.
The findings of this research have the potential to
transform the field of reproductive medicine, making
fertility treatments more accessible, cost-effective, and
successful. Utilizing the capabilities of deep learning, this
research marks a hopeful stride in enhancing the
prospects of conception for individuals and couples
grappling with infertility.
Keywords :
Fertility treatments, In vitro fertilization (IVF), Embryo quality, Embryo assessment, Convolutional Neural Network (CNN), Deep learning, Reproductive medicine, Automated assessment, Embryo viability, Reproductive health.
