Publication Date: 2023/12/11
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.
DOI: https://doi.org/10.5281/zenodo.10353687
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV2285.pdf
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