Volume 9, Issue 2 (2024)
SJMR 2024, 9(2): 105-117 |
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Nateghi M R, Nikzad H, Hassani Bafrani M. The use of Machine Learning for Human Sperm Selection and Success Rate in IVF Methods. SJMR 2024; 9 (2) : 6
URL: http://saremjrm.com/article-1-336-en.html
URL: http://saremjrm.com/article-1-336-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: (734 Views)
Objective: Infertility is indeed a significant global health concern. The quality of gametes plays a pivotal role in determining the success rates of Assisted Reproductive Technology (ART) cycles. In contemporary fertility and reproductive medicine, the utilization of machine learning has emerged as a powerful tool for processing large datasets, offering the potential to enhance existing ART practices. The objective of this review study was to assess and quantify sperm and oocyte characteristics in humans using machine learning techniques. This approach can contribute to a more precise evaluation of gamete quality, leading to improved decision-making and potentially higher success rates in ART procedures. Using of machine learning abilities, researchers can obtain valuable insights into the quality of gametes, thereby optimizing fertility treatments for individuals and couples experiencing infertility issues.
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: 6
Keywords: Machine Learning , Artificial Intelligence , Deep Learning , In Vitro Fertilization , Sperm
Article Type: Systematical Review |
Subject:
Sterility
Received: 2024/08/1 | Accepted: 2024/08/31 | Published: 2024/12/19
Received: 2024/08/1 | Accepted: 2024/08/31 | Published: 2024/12/19
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