IVF success could be increased using AI tools

NIHR-funded research has discovered that artificial intelligence (AI) could help doctors improve the success rates of in-vitro fertilisation (IVF) treatment.

A study found that AI could assist in selecting the best moment to target follicles - small sacs in the ovaries containing eggs - in order to increase the likelihood of successful IVF treatment. 

The research was funded by the NIHR Imperial Biomedical Research Centre and UK Research and Innovation, and led by researchers at Imperial College London, University of Glasgow and University of St Andrews, and clinicians at Imperial College Healthcare NHS Trust. It has been published in Nature Communications.

How IVF works

Infertility affects one in six individuals and couples, often leading them to seek IVF treatment to conceive. Current IVF success rates depend largely on the age of the women undergoing treatment, with the highest pregnancy rates among patients aged 18-34 at 42%. 

During IVF treatment, doctors use ultrasound scans to monitor the size of follicles to decide when to give a hormone injection known as the ‘trigger’. This prepares the eggs for collection and ensures they are ready to be fertilised with sperm to create embryos. 

The timing of the trigger administration is crucial, as it is less effective if the follicles are too small or too large. After egg collection and fertilisation, an embryo is selected and implanted into the womb, which hopefully leads to pregnancy.

Using AI to personalise IVF treatments

This research used ‘Explainable AI’ techniques - a type of AI that allows humans to understand how it works - to analyse retrospective data on more than 19,000 patients who had completed IVF treatment. The researchers explored which follicle sizes were associated with improved rates of retrieving mature eggs to result in babies being born. 

Currently, clinicians use ultrasound scans to measure the largest follicles and administer the trigger injection when two or three follicles exceed 17-18mm. However, their findings suggest that maximising the proportion of intermediate-sized follicles targeted with the hormone injection (sized between 13mm and 18mm) could optimise egg retrieval and increase birth rates. 

Professor Waljit Dhillo, co-senior author of the study, Dean of the NIHR Academy and NIHR Scientific Director for Research Capacity and Capabilities, said: “Our study is the first to analyse a large dataset to show that AI can identify the specific follicle sizes that are most likely to yield mature eggs more precisely than current methods.

“This is an exciting development as the findings suggest that we can use information from a much wider set of follicle sizes to decide when to give patients trigger shots rather than just the size of only the largest follicles - which is what is used in current clinical practice.”

Dr Ali Abbara, NIHR Clinician Scientist at Imperial College London and Consultant in Reproductive Endocrinology at Imperial College Healthcare NHS Trust, and co-senior author of the study, said: “In future, AI could be used to provide accurate recommendations to improve decision-making and aid in personalisation of treatment, so that we can give each couple the very best possible chance of having a baby.”

The team plan to create an AI tool that will utilise findings from their research to personalise IVF treatment. The tool could help support clinicians’ decision making at each step of the IVF process. They will apply for funding to take this tool into clinical trials.