Use of federated learning to develop an artificial intelligence model predicting usable blastocyst formation from pre-ICSI oocyte images

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Research questionCan Federated Learning be used to develop an artificial intelligence (AI) for evaluating oocyte competence using two-dimensional images of denuded oocytes in metaphase II prior to intracytoplasmic sperm injection (ICSI)? Design10,677 oocyte images with associated metadata were prospectively collected by 8 in vitro fertilization (IVF) clinics across 6 countries. AI training used Federated Learning, where data were retained on regional servers to comply with data privacy laws. The final AI required a single image as input to evaluate oocyte competence, which was defined by the formation of a usable blastocyst (≥expansion grade 3 by Day 5/6 post-ICSI). ResultsThe oocytes AI demonstrated area under the curve (AUC) up to 0.65 on two blind test datasets. A high sensitivity for predicting competent oocytes (83-88%) was offset by a lower specificity (26-36%). Exclusion of confounding biological variables (male factor infertility and maternal age ≥35 years) improved AUC up to 14%, primarily due to increased specificity. AI scores correlated with size of the zona pellucida and perivitelline space, and ooplasm appearance. AI scores also correlated with blastocyst expansion grade and morphological quality. The sum of AI scores from oocytes in group culture images predicted formation of ≥2 usable blastocysts (AUC=0.77). ConclusionAn AI for evaluating oocyte competence was developed using Federated Learning, representing an essential step in protecting patient data. The AI was significantly predictive of oocyte competence as defined by usable blastocyst formation, which is a critical factor for IVF success. Potential clinical utility ranges from selective oocyte fertilization, to guiding treatment decisions regarding additional rounds of oocyte retrieval.