Supporting first FSH dosage for ovarian stimulation with machine learning

Abstract

Research question: Is it possible to identify accurately the optimal first dose of FSH in ovarian stimulation by means of a machine learning model?

Design: Observational study (2011-2021) including first IVF cycles with own oocytes. A total of 2713 patients from five private reproductive centres were included in the development phase (2011-2019) and 774 in the validation phase (2020-2021). Predictor variables included age, BMI, AMH, AFC and previous live births. Performance was measured with a proposed score based on the number of MII oocytes retrieved and dose received, recommended, or both.

Results: The included cycles were from women aged 37.7 ± 4.4 years (18-45 years), with a BMI of 23.5 ± 4.2 kg/m², AMH of 2.4 ± 2.3 ng/ml, AFC of 11.3 ± 7.6, and an average number of MII obtained 6.9 ± 5.4. The model reached a mean performance score of 0.87 (95% CI 0.86 to 0.88) in the development phase, significantly better than for doses prescribed by clinicians for the same patients (0.83, 95% CI 0.82 to 0.84; P = 2.44 e-10). Mean performance score of the model recommendations was 0.89 (95% CI 0.88 to 0.90) in the validation phase, also significantly better than clinicians (0.84, 95% CI 0.82 to 0.86; P = 3.81 e-05). The model was shown to surpass the performance of standard practice.

Conclusion: This machine learning model could be used as a training and learning tool for new clinicians, and as quality control for experienced clinicians.

Keywords: Artificial intelligence; Machine learning; Ovarian stimulation; Prediction.