During IVF treatment, doctors use ultrasound scans to monitor the size of follicles – small sacs in the ovaries containing eggs – to decide when to give a hormone injection known as the ‘trigger’ to prepare the eggs for collection and ensure that they are ready to be fertilised with sperm to create embryos. The timing of the trigger is a key decision, as it works less effectively if the follicles are too small or too large at the time of administration. After the eggs are collected and fertilised by sperm, an embryo is then selected and implanted into the womb to hopefully lead to pregnancy.
Researchers 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. They explored which follicle sizes were associated with improved rates of retrieving mature eggs to result in babies being born.
They found that delivering the hormone injection when a greater proportion of follicles were sized between 13-18mm was linked to higher rates of mature eggs being retrieved and improved rates of babies being born.
Personalisation of IVF treatment
Currently, clinicians use ultrasound scans to measure the size of the lead (largest) follicles and generally give the trigger injection when a threshold of either two or three lead follicles greater than 17 or 18mm has been reached.
Their findings suggest that maximising the proportion of intermediately sized follicles could optimise the number of mature eggs retrieved and improve the rates of babies being born.
The team believe that the findings from the study highlight the potential of AI to aid in the personalisation of IVF treatment to improve clinical outcomes for patients and maximise their chance of taking home a baby. The team plan to create an AI tool that will utilise findings from their research to personalise IVF treatment and support clinicians’ decision making at each step of the IVF process. They will apply for funding to take this tool into clinical trials.
The research, published in Nature Communications, is led by researchers at Imperial College London, University of Glasgow, University of St Andrews, and clinicians at Imperial College Healthcare NHS Trust. It is funded by UK Research and Innovation and the National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre (BRC).
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:
“IVF provides help and hope for many patients who are unable to conceive but it’s an invasive, expensive, and time-consuming treatment. It can be heartbreaking when it fails, so it’s important to ensure that this treatment is as effective as possible.
“AI can offer a new paradigm in how we deliver IVF treatment and could lead to better outcomes for patients.
“IVF produces so much rich data that it can be challenging for doctors to fully make use of all of it when making treatment decisions for their patients. Our study has shown that AI methods are well suited to analysing complex IVF data. 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.”
