Artificial intelligence in human in vitro fertilization and embryology

Area of Science:

• Reproductive Medicine
• Biotechnology
• Artificial Intelligence

Background:

• Embryo evaluation and selection are critical steps in in vitro fertilization (IVF), aiming to identify viable embryos for implantation.
• Current methods, including morphology, time-lapse imaging, and preimplantation genetic testing, have limitations in predicting implantation success.
• There is a need for novel technologies to improve embryo selection and increase live birth rates.

Purpose of the Study:

• To provide an overview of current artificial intelligence (AI) technologies in reproductive medicine.
• To explore the potential future applications of AI in enhancing IVF processes.
• To highlight AI's role in improving embryo evaluation and patient-specific treatments.

Main Methods:

• Review of existing AI-based methods for human embryo evaluation.
• Discussion of AI applications in assessing reproductive potential and optimizing stimulation protocols.
• Exploration of AI's capability to analyze large datasets (embryological, clinical, genetic).

Main Results:

• AI methods are emerging as objective, standardized, and efficient tools for human embryo assessment.
• AI can be applied to various aspects of IVF, including patient assessment and treatment individualization.
• AI's ability to analyze big data is key to achieving patient-tailored treatments.

Conclusions:

• AI technologies hold significant promise for advancing embryo evaluation and selection in IVF.
• The integration of AI can lead to more personalized and effective fertility treatments.
• Future applications of AI in reproductive medicine aim to optimize all aspects of the IVF process for improved success rates.