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Introduction
A recent review published in Fertility and Sterility examined the potential applications of artificial intelligence (AI) and machine learning in sperm selection. With over 100 million individuals globally facing issues related to infertility, semen analysis is essential for the diagnosis and subsequent treatment of male factor infertility. However, selecting a single sperm from millions in a sample using the conventional approach is a laborious process at a high risk of selection errors. The final selection is primarily performed manually by embryologists, leading to a low success rate of assisted reproductive technologies (ART). Herein, AI and machine learning could significantly improve the efficiency of the ART laboratory.
Importance of Sperm Selection
Semen analysis is essential for diagnosing male factor infertility, and selecting a single sperm from millions is a daunting task. Despite technological advancements, sperm selection is primarily performed manually by embryologists following the World Health Organization (WHO) criteria. Therefore, AI and machine learning could significantly improve the efficiency of the ART laboratory.
Importance of Semen Analysis
Semen parameters are strong prognostic indicators of fertilization and pregnancy outcomes. If the semen parameters are suboptimal, ART can help the sperm overcome the female reproductive tract barrier, improving the likelihood of conception.
Current Challenges in Sperm Selection
Embryologists face the challenge of selecting favorable sperm from a large sample size of semen. Sperm selection is crucial as a single sperm is required for intracytoplasmic sperm injection (ICSI).
AI and Machine Learning for Sperm Selection
AI and machine learning algorithms can standardize and expedite sperm analyses based on the available models. The algorithms can process large datasets, reduce embryologist time and effort, and automate the sperm selection process by coupling genetic and visual data.
Standardized Selection Criteria
AI algorithms can significantly improve sperm analysis accuracy by evaluating sperm morphology in combination with deep learning algorithms. Implementing AI and machine learning algorithms could significantly improve the embryologist’s sperm selection capabilities.
Accuracy and Quality of Training Datasets
The performance of AI and machine learning algorithms depends on the quality of the training dataset images. To obtain greater accuracy in these systems, they must be trained with larger and high-quality sperm imaging data.
Standardized DNA Fragmentation Evaluation
Male fertility is inversely associated with DNA fragmentation, which is crucial for sperm selection. Machine learning algorithms, trained using sperm images and appropriate DNA fragmentation index (DFI) values, can accurately evaluate the quality of a single sperm based on the trained dataset.
Applications in Sperm Motility Evaluation
Holographic imaging, computer-aided sperm analysis (CASA), and microfluidic platforms are used by embryologists to determine sperm motility. Applications of AI and machine learning techniques significantly improve conception rates and successful pregnancy outcomes following ART by training the AI system with multiple data on sperm motility.
Conclusion
With over 100 million individuals globally facing issues related to infertility, AI and machine learning techniques could significantly improve the efficiency of the ART laboratory. Machine learning and AI algorithms can process large datasets, reduce the embryologist’s time and effort, automate the sperm selection process, and significantly improve conception rates and successful pregnancy outcomes following ART.
FAQ
What is semen analysis, and why is it essential?
Semen analysis is essential for the diagnosis and subsequent treatment of male factor infertility. Semen parameters are strong prognostic indicators of fertilization and pregnancy outcomes.
Why is selecting a single sperm from millions in a sample a daunting task?
Selecting a single sperm from millions in a sample using the conventional approach is a laborious process at a high risk of selection errors.
Why is sperm selection crucial?
Sperm selection is crucial as a single sperm is required for intracytoplasmic sperm injection (ICSI).
How can AI and machine learning algorithms improve sperm analysis?
AI and machine learning algorithms can standardize and expedite sperm analyses based on the available models. Moreover, machine learning algorithms trained using sperm images and DNA fragmentation index values can accurately evaluate the quality of a single sperm based on the trained dataset. Holographic imaging, computer-aided sperm analysis (CASA), and microfluidic platforms can be used to determine sperm motility, and applications of AI and machine learning techniques significantly improve conception rates and successful pregnancy outcomes following ART.
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