Application of artificial intelligence in gametes and embryos selection

Area of Science:

• Reproductive biology and medicine
• Medical artificial intelligence
• Bioinformatics

Background:

• Assisted Reproductive Technology (ART) success hinges on gamete and embryo quality.
• Current methods for assessing sperm, oocyte, and embryo quality lack sufficient accuracy and objectivity.
• There is a need for advanced tools to improve gamete and embryo selection in ART.

Purpose of the Study:

• To review the recent advancements in Artificial Intelligence (AI) applications for gamete and embryo assessment.
• To explore the utility of AI in semen analysis and the selection of sperm, oocytes, and embryos.
• To discuss current challenges and future directions for AI in reproductive medicine.

Main Methods:

• Review of current literature on AI applications in ART.
• Analysis of AI models for image and video analysis of gametes and embryos.
• Synthesis of findings on AI performance in semen analysis and gamete/embryo selection.

Main Results:

• AI demonstrates significant potential in analyzing large datasets, particularly images and videos, for gamete and embryo assessment.
• Well-trained AI models exhibit high accuracy and speed, aiding embryologists in objective selection.
• Various AI models have been developed, with several showing promising performance in gamete and embryo evaluation.

Conclusions:

• AI technology is a valuable tool for improving the accuracy and objectivity of gamete and embryo assessment in ART.
• AI applications in semen analysis and gamete/embryo selection are rapidly evolving.
• Further research and development are needed to address existing challenges and optimize AI's role in reproductive technologies.