How AI is helping in identifying embryos during IVF? answers IVF specialist Dr Gautam Allahbadia

IVF specialist
Doctor woman offering medical advices to a young couple in office

Scientists around the world have developed a new artificial intelligence system that might help in predicting embryos will grow into healthy babies during the IVF procedure. At present embryologists are employed to choose those fertilized eggs that are most likely to result in a pregnancy. This involves checking them by eye to see which appear to be healthy.
In the late 90s and early 2000s, there was a rise in success rates of IVF due to better understanding of the nutritional and environmental needs of the embryo. Research is currently focusing on methods to further improve the success rates of IVF.

Scientific advancements in the field of reproductive technology, are making procedures safer, efficient, affordable and accessible. Aneuploidy screening and genetic testing of embryos prior to their implantation became a routine in the last couple of years to increase the chances of a healthy pregnancy. However, some recent developments are taking it one step ahead.

According to Dr Gautam Allahbadia, an IVF specialist in Mumbai, a number of approaches are currently being explored for improving the success rates of IVF, which may rise through better understanding of embryo quality. Thus, resulting in a quick identification of a healthy embryo for transfer.

Deep learning and AI

Deep learning is an approach of Artificial Intelligence that is roughly modeled after the neural networks of the brain, which analyzes information in increasing layers of complexity. When a computer is fed with new information, its ability to recognize the desired patterns improves automatically.The researchers are trying to tailor the process for the individual patients.

AI systems for IVF are still in the experimental phase, but the results so far have been promising. AI technologies have remarkable potential to transcend the narrow focus on individual embryos and discover new patterns hidden in the patient data for the treatment of infertility.w