Unpacking the artificial intelligence toolbox for embryo ploidy prediction

Munevver Serdarogullari, George Liperis, Kashish Sharma, Omar F. Ammar, Julia Uraji, Danilo Cimadomo, Alessandra Alteri, Mina Popovic, and Juan J. Fraire-Zamora

Human Reproduction, Volume 38, Issue 12, December 2023, Pages 2538–2542, https://doi.org/10.1093/humrep/dead223

Published: 24 October 2023


Bamford et al., (2023) evaluated 12 ML models for blastocyst ploidy prediction, finding logistic regression surpassed more complex algorithms.

The role of age in ML models

  • Bamford’s model was trained in a population of older oocyte providers
  • Medical datasets have inherent biases based on the population sampled
  • ML model generalisation across ages is questioned
  • The role of oocyte age in ML depends on the statistical unit (population vs. cohort)

From dataset imbalances to clinical decisions

  • Dataset imbalances impact ML clinical generalisation
  • Interdisciplinary teamwork is key for ML-based decision making
  • Simple, interpretable models can outperform complex ones
  • Clinicians must grasp ML methods for successful IVF integration

Using ML to revitalise the ‘classics’

• Bamford’s model using clinical variables and embryo morphology is a valid alternative for clinics without timelapse technology

• The goal of ML-based embryo selection should be to predict a live birth

• ML-based embryo assessment must be used as a tool for clinical decisions without replacing professionals


The May ESHRE Journal Club discussion focused on a study by Bamford et al. (2023), ML models for predicting ploidy status of embryos, IVF dataset characteristics and imbalances, and the use of ML-based embryo assessment as a tool for clinical decisions. ML, machine learning.

Add Comment

Your email address will not be published. Required fields are marked *