Can AI Improve the Success of IVF Treatment? Study Determines - Video

A new study found that during IVF, 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. The findings are published in Nature Communications.

Researchers used ‘Explainable AI’ techniques - a type of AI that allows humans to understand how it works - to analyse retrospective data. They explored which follicle sizes were associated with improved rates of retrieving mature eggs to result in babies being born.

Their findings suggest that maximising the proportion of intermediately sized follicles could optimise the process.

In the retrospective study, the team used AI techniques on data from 19,082 patients aged between 18-49 years of age who had treatment in one of 11 clinics between 2005-2023. They examined individual follicle sizes on the days before and on the day of trigger administration.

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.”

Reference: Hanassab, S., Nelson, S.M., Akbarov, A. et al. Explainable artificial intelligence to identify follicles that optimize clinical outcomes during assisted conception. Nat Commun 16, 296 (2025). https://doi.org/10.1038/s41467-024-55301-y