Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble

Key points

• We aimed to construct a curated single-centre IVF/ICSI dataset and develop a prediction model suited to small tabular clinical data.1
• Materials and Methods: We assembled 616 fresh embryo-transfer cycles (251 live birth, 365 non-live birth) with clinical, laboratory, and embryological predictors, and embryo morphology encoded with a novel differential weighting of the inner cell mass and trophectoderm.1
• The differentially weighted trophectoderm feature ranked highest by consensus importance across models.1
• Conclusion: The curated dataset and HAN + LR ensemble offer a transparent, reproducible basis for IVF/ICSI live-birth prediction; external validation is the next step.1

Sources (1)

• [1]Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 02:43 AM

  Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous, and contain pathway-dependent variables prone to outcome leakage.

  We aimed to construct a curated single-centre IVF/ICSI dataset and develop a prediction model suited to small tabular clinical data.

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