Dr José Antonio Ortiz will present a predictive model of ovarian response based on machine-learning at the SEF congress

Dr José Antonio Ortiz, molecular biologist at Instituto Bernabeu, will present a novel predictive model of ovarian response based on machine-learning at the congress of the Spanish Fertility Society (SEF in Spanish). This model allows for a more accurate identification of patients who are at greater risk of suboptimal ovarian response or hyper-response during ovarian stimulation.

Ovarian response is a key factor in the success of assisted reproduction treatment (IVF). However, there is great variability between patients, even among those with similar ovarian reserve. This variability can make it difficult to choose the most appropriate ovarian stimulation protocol, which can negatively affect pregnancy rates.

The study by Dr Ortiz and his team has identified 5 genetic variants that play an important role in the variability of ovarian response. Two of these variants are found in hormone receptors (oestrogen receptor and anti-Müllerian hormone receptor), one in an oestrogen biosynthesis enzyme (CYP19A1) and the remaining two in proteins with cellular protective functions (TP53 and SOD2).

“The use of machine-learning in this field is a very powerful tool that allows us to analyse large amounts of data and find patterns that would otherwise be difficult to identify,” says Dr Ortiz. “This study is an important step towards precision medicine in the field of assisted reproduction. But our goal is to continue researching along these lines in order to offer our patients the best possible treatment options,” he adds.