Artificial Intelligence (AI) has been a big game-changer in all industries around the world. Low success rates, as well as multiple cycle failures for attaining successful IVF treatment, have led to the exploration of AI in the field of Assisted Reproductive Technology (ART). One of the crucial determinants for a successful IVF treatment is the quality of embryos selected for embryo transfer, which is currently a manual process where well-experienced embryologist selects embryos via visual inspection. AI-based embryo selection has major advantages over the manual selection process and now it has become the new normal for clinical embryologists worldwide. At our Centres in Delhi with this top-notch technology, we are able to increase our success rate as well as reduce the number of attempts required for conception. Also, this service helps dramatically in the decision of which embryos are worth cryopreservation to give a second fair chance at conception or for a second baby. 7 reasons why we need to opt for “AI-based embryo selection” are:
- 15% reduction in time to conceive.
With a better selection of viable embryos via AI for 1st attempt of embryo transfer, the chances of conception are higher. This allows to reduce the chances of multiple attempts as well as reduce the number of embryos transferred per cycle.
- Trained tool to visualize vital morphological features
A single embryo has thousands of markers or indicators showing its potential of being a viable or non-viable embryo. During the development of AI for viable embryo selection, multiple data on embryos were collected from the best IVF clinics all over the world and compiled. This exercise led to the discovery of unknown patterns and complex features which are practically impossible to be seen by the human eye but to an AI with machine learning, it was easily able to correlate these features from different parts of embryo image. Since the embryo selection process followed by skilled clinical embryologists has quite a few limitations, AI with its deep learning combined with computer visual image processing is able to better identify all the markers better.