Table of Content:
- Introduction to In Vitro Fertilization (IVF)
- Future of Assisted Reproductive Technology(ART)
- Dr Gautam Allahbadia
- Artificial Intelligence in IVF
- Role of Deep learning in IVF
There have been tremendous advances in the field of assisted reproduction, since the birth of Louise Brown, the first baby conceived through IVF in 1978. These advances are the collective result of techniques known as Assisted Reproductive Technologies (ART). The advent of In Vitro Fertilization (IVF) over 30 years ago has made the oocyte and preimplantation embryo uniquely accessible.
In the late 90s and early 2000s, there was an increase in the success rates of IVF due to the greater understanding of the nutritional and environmental needs of the embryo. Research is currently focusing on methods to further improve the IVF success rate.
Scientific advancements are rapidly changing the field of reproductive technology, 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, recent developments are taking it one step further.
At this juncture, IVF treatment has become common. Around 1.7 per cent of the babies in USA are born through assisted reproduction. So what does the future hold now for ART?
According to Dr Gautam Allahbadia, an IVF specialist in Mumbai, , a number of approaches are currently being explored for further improvements in success rate, which may rise through better understanding of embryo quality. Thus, resulting in a quick identification of the right embryo for transfer.
AI in Embryo Identification:
While researchers have been using new imaging and sophisticated genetic techniques to study the unfertilized egg and predict the genetic makeup of embryos, there is a possible scope that AI-integration in the process could increase accuracy.
Analysis of the data proved that Machine Learning Algorithms applied to large clinical data sets could predict the outcome of the IVF treatment with high accuracy
Artificial Intelligence systems might be able to flag the most viable embryos far better than humans. They can identify the accuracy of a 5-day-old, In Vitro fertilized human embryo’s potential to progress to a successful pregnancy. The technique, which analyzes time-lapse images of the early-stage embryos, could improve the success rate of IVF and minimize the risk of multiple pregnancies.
Choosing the embryo with the best chances of developing into a healthy pregnancy is currently a subjective process. “By introducing new technology into the field of IVF, we can automate and standardize a process that was dependent on subjective human judgement,” says Dr. Gautam Allahbadia, one of the top infertility doctors in Mumbai.
The role of Deep Learning:
Deep learning is an Artificial Intelligence approach 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.
It can identify the features of a healthy embryo, whilst avoiding unhealthy ones. The size of the training data set is critically important to the success of the algorithm, with more data leading to better outcomes.
For maximizing the chances of having one successful birth, fertility specialists often implant multiple embryos, but the process is imprecise and can result in multiple pregnancies. It can lead to complications, such as low birth weight, premature delivery and other maternal problems. Thus the investigators developed another computational approach that can take maternal age and the quality of multiple embryos into account for determining the best combination to achieve a single live birth.
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.
