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<p>AI models for embryo selection often rely on correlational metrics that ignore clinical confounders. We present a target trial emulation framework to approximate causal effects for a foundational AI model, FEMI, for non-invasive embryo assessment using multi-center trial emulation (<i>n</i> = 4674). Propensity score matching established a robust association between FEMI-Ploidy and implantation failure showing an average treatment effect (ATE) of −0.131 (95% CI [−0.196, −0.066]) in the development cohort and −0.157 (95% CI [−0.254, −0.054]) in the external cohort. Comparative efficacy using S-Learner models demonstrated that a high-risk FEMI score carried a significantly stronger individual treatment effect (ITE) penalty on implantation compared to other scoring mechanisms (<i>p</i> &lt; 0.0001). This superiority persisted after adjusting for maternal age, suggesting FEMI captures unique biological features. This causal framework establishes a rigorous standard for AI validation in IVF, providing the necessary pre-clinical justification for prospective randomized controlled trials.</p>

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Trial emulation for validating the clinical efficacy of a foundational AI model in embryo selection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月23日

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  • Suraj Rajendran,
  • Jonas E. Malmsten,
  • Lorena B. Arnal,
  • Marcos Meseguer,
  • Zev Rosenwaks,
  • Nikica Zaninovic,
  • Iman Hajirasouliha

摘要

AI models for embryo selection often rely on correlational metrics that ignore clinical confounders. We present a target trial emulation framework to approximate causal effects for a foundational AI model, FEMI, for non-invasive embryo assessment using multi-center trial emulation (n = 4674). Propensity score matching established a robust association between FEMI-Ploidy and implantation failure showing an average treatment effect (ATE) of −0.131 (95% CI [−0.196, −0.066]) in the development cohort and −0.157 (95% CI [−0.254, −0.054]) in the external cohort. Comparative efficacy using S-Learner models demonstrated that a high-risk FEMI score carried a significantly stronger individual treatment effect (ITE) penalty on implantation compared to other scoring mechanisms (p < 0.0001). This superiority persisted after adjusting for maternal age, suggesting FEMI captures unique biological features. This causal framework establishes a rigorous standard for AI validation in IVF, providing the necessary pre-clinical justification for prospective randomized controlled trials.