Volume 9, Issue 2 (2024)
SJMR 2024, 9(2): 93-104 |
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Nateghi M R, Nikzad H, Hassani Bafrani M. The use of Machine Learning for Human Oocyte selection and Success Rate in IVF Methods. SJMR 2024; 9 (2) : 5
URL: http://saremjrm.com/article-1-335-en.html
URL: http://saremjrm.com/article-1-335-en.html
1- Sarem Gynecology, Obstetrics and Infertility Research Center, Sarem Women’s Hospital, Iran University of Medical Science (IUMS), Tehran, Iran. & Sarem Cell Research Center (SCRC), Sarem Women’s Hospital, Tehran, Iran.
2- Gametogenesis Research Center, Kashan University of Medical Science, Kashan
3- Student Research Committee, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
2- Gametogenesis Research Center, Kashan University of Medical Science, Kashan
3- Student Research Committee, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
Abstract: (503 Views)
Objective: In vitro fertilization (IVF) methods are among the primary solutions for addressing infertility, but the success of these methods depends on the precise selection of high-quality oocytes. In this regard, machine learning, as an innovative technology, can play a crucial role in improving the egg selection process. Machine learning algorithms analyze microscopic images and related data to identify key characteristics of the oocytes and select those with higher potential for successful fertilization. Utilizing this technology not only enhances the accuracy of egg selection and reduces human errors but also minimizes the time and costs associated with manual testing. This paper examines the advantages, methods used, challenges, and future potential of applying machine learning in the selection of human oocytes to increase the success rate of IVF methods. The results indicate that advancements in this field can significantly improve the success rate and efficiency of artificial insemination methods.
Materials and Methods: We conducted a comprehensive search on PubMed, Google Scholar, and Scopus using the keywords "Machine Learning AND Quantification AND IVF." Eligible articles were initially screened based on their titles. After the title screening, a second screening was performed based on the abstracts of the selected articles. Finally, the full articles of the remaining studies were reviewed to ensure they met our inclusion criteria. From each eligible study, we extracted the following information: author(s) of the study, publication year, and the method employed to evaluate human oocyte quality.
Conclusion: The development of a properly trained machine learning system will require careful attention to data quality, measurement, sample size and ethics issues agreement.
Materials and Methods: We conducted a comprehensive search on PubMed, Google Scholar, and Scopus using the keywords "Machine Learning AND Quantification AND IVF." Eligible articles were initially screened based on their titles. After the title screening, a second screening was performed based on the abstracts of the selected articles. Finally, the full articles of the remaining studies were reviewed to ensure they met our inclusion criteria. From each eligible study, we extracted the following information: author(s) of the study, publication year, and the method employed to evaluate human oocyte quality.
Conclusion: The development of a properly trained machine learning system will require careful attention to data quality, measurement, sample size and ethics issues agreement.
Article number: 5
Keywords: Machine Learning, Artificial Intelligence, Deep Learning, In Vitro Fertilization, Sperm, Oocyte.
Article Type: Systematical Review |
Subject:
Sterility
Received: 2024/08/5 | Accepted: 2024/08/20 | Published: 2024/12/19
Received: 2024/08/5 | Accepted: 2024/08/20 | Published: 2024/12/19
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