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Artificial Intelligence in In Vitro Fertilisation

The most successful form of assisted reproductive technology is in vitro fertilisation (IVF). It is a sophisticated procedure designed to increase fertility, prevent genetic issues, and aid conception. IVF involves taking mature eggs out of the ovaries and fertilising them with sperm in a lab. After that, the fertilised egg (embryo) or eggs are implanted within the uterus. One whole cycle of IVF takes about three weeks to complete. Processing time may increase if these methods are broken step by step.

Artificial intelligence (AI) has gained traction in forecasting healthcare outcomes utilising frequently acquired data such as patient characteristics, medical imaging, and blood test results. It has consequently produced an obvious application in the precise classification of cancer severity utilising medical imaging.

The most prevalent applications of AI in the field of reproductive health are consumer apps like Flo, which use technology to enhance forecasts of ovulation and fertility based on self-reports of menstrual cycles, physical symptoms, sexual behaviour, and more. The AI-powered Mira is another option. In addition to requesting health-related data from users, Mira conducts a home urine test that enables the app to analyse hormone levels and provide users with tailored reproductive guidance. One of the most promising applications of AI is in vitro fertilisation (IVF), the most common type of artificial reproductive technology.

AI in gamete selection

The assessment of sperm morphology, DNA integrity, and sperm selectivity has also been done using artificial intelligence. The evaluation of human oocytes, normal fertilisation prediction, evaluation of embryo development to the blastocyst (BL) stage assessment, and analysis of implantation potential using static oocyte images have all been attempted in the early stages of artificial intelligence (AI) approaches.

Artificial intelligence (AI) will objectively evaluate gametes based on quality and remove the subjectivity of human judgment from the decision-making process. The greatest benefit of AI may come from the selection of sperm for intracytoplasmic sperm injection (ICSI), a procedure currently performed by the embryologist. AI can calculate the optimal sperm-egg combination to achieve the highest success rate or determine whether IVF or ICSI is the best fertilisation method. AI has also proven adept at predicting fertility outcomes based on different ultrastructural details of sperm.

AI-based techniques can be used for other IVF clinical facets, like determining a patient’s chance of becoming pregnant and customising gonadotropin stimulation regimens. Since AI can analyse “big” data, the ultimate objective is to use AI tools to analyse all clinical, genomic, and embryological data to give each patient a customised course of treatment.

AI in embryo selection

Embryo evaluation and selection encompasses the overall performance of the entire in vitro fertilisation (IVF) process. It seeks to choose the “best” embryos from a vast pool of fertilised oocytes, most of which will be found to be non-viable due to aberrant development or chromosomal is challenging to estimate implantation rates in humans, even after embryo selection based on morphology, time-lapse microscopic imaging, or embryo biopsy in conjunction with pre-implantation genetic testing. Technologies such as AI must be used to improve embryo evaluation and selection and increase live birth rates

The current focus of AI applications in embryology can be classified into the following groups: automated interpretation of embryo development (cell stages and cell cycles), embryo grading (mostly at the BL stage), and embryo selection for implantation.

The use of AI-based analysis to predict embryo ploidy is now under investigation.

AI study of embryos could be a beneficial technique for anticipating miscarriages. Artificial intelligence can be used to identify uterine anomalies and evaluate endometrial disorders. Additionally, AI assessment of the endometrium can be combined with implantation data to automate endometrial receptivity analysis.

The application of artificial intelligence and digital technologies in reproductive medicine has grown during the last three to five years. Approaches based on artificial intelligence are anticipated to be quicker, more accurate, and more objective. Although artificial intelligence (AI) applications in embryology have drawn the most interest and show the most promise, their usage is likely to spread to other facets of reproductive health.