RESEARCH ARTICLE
Time Series Prediction of Viable Embryo and Automatic Grading in IVF using Deep Learning
Gargee Vaidya1, Shreya Chandrasekhar1, Ruchi Gajjar1, *, Nagendra Gajjar1, Deven Patel2, Manish Banker2
Article Information
Identifiers and Pagination:
Year: 2021Volume: 15
Issue: Suppl-2, M8
First Page: 190
Last Page: 203
Publisher ID: TOBEJ-15-190
DOI: 10.2174/1874120702115010190
Article History:
Received Date: 05/09/2020Revision Received Date: 22/11/2020
Acceptance Date: 16/12/2020
Electronic publication date: 31/12/2021
Collection year: 2021
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
The process of In Vitro Fertilization (IVF) involves collecting multiple samples of mature eggs that are fertilized with sperms in the IVF laboratory. They are eventually graded, and the most viable embryo out of all the samples is selected for transfer in the mother’s womb for a healthy pregnancy. Currently, the process of grading and selecting the healthiest embryo is performed by visual morphology, and manual records are maintained by embryologists.
Objectives:
Maintaining manual records makes the process very tedious, time-consuming, and error-prone. The absence of a universal grading leads to high subjectivity and low success rate of pregnancy. To improve the chances of pregnancy, multiple embryos are transferred in the womb elevating the risk of multiple pregnancies. In this paper, we propose a deep learning-based method to perform the automatic grading of the embryos using time series prediction with Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN).
Methods:
CNN extracts the features of the images of embryos, and a sequence of such features is fed to LSTM for time series prediction, which gives the final grade.
Results:
Our model gave an ideal accuracy of 100% on training and validation. A comparison of obtained results is made with those obtained from a GRU model as well as other pre-trained models.
Conclusion:
The automated process is robust and eliminates subjectivity. The days-long hard work can now be replaced with our model, which gives the grading within 8 seconds with a GPU.
