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Area of Science:

  • Reproductive endocrinology and infertility research.
  • Application of artificial intelligence in clinical decision support systems.
  • Biostatistics and predictive modeling in healthcare.

Background:

  • Accurate prediction of ovarian response and optimal follicle-stimulating hormone (FSH) dosage are crucial for effective ovarian stimulation.
  • Current methods lack a comprehensive model for simultaneously predicting oocyte yield and ovarian hyperstimulation syndrome (OHSS) risk.

Purpose of the Study:

  • To establish an integrated predictive model for forecasting the number of oocytes retrieved (NOR) and assessing early-onset moderate-to-severe OHSS risk.
  • To guide optimal starting doses of FSH in individualized ovarian stimulation protocols.

Main Methods:

  • Prognostic study involving patients undergoing their first ovarian stimulation cycles.
  • Development and validation of machine learning models (11 for NOR, 11 for OHSS) using large internal and external datasets.
  • Application of Shapley additive explanation for variable interpretation and development of a web-based prediction tool (InOvaSGuide).

Main Results:

  • Gradient boosting regressor achieved high performance for NOR prediction (R² ≈ 0.79).
  • LightGBM model showed superior performance for OHSS prediction (AUC ≈ 0.73-0.76).
  • Key predictors identified include FSH starting dose to BMI ratio for NOR and baseline antral follicle count for OHSS.

Conclusions:

  • An integrated framework for predicting NOR and OHSS risk across varying FSH doses has been developed.
  • The predictive models were implemented in a user-friendly online tool, InOvaSGuide.
  • Further prospective evaluation is recommended prior to clinical implementation.