A machine learning model for predicting fertilization following short-term insemination using embryo images.

ABSTRACT:

Purpose

This study established a machine learning model (MLM) trained on embryo images to predict fertilization following short-term insemination for early rescue ICSI and compared its predictive performance with the embryologist's manual classification.

Methods

Embryo images at 4.5 and 8 h post-insemination were preprocessed into vectors using ResNet50. The Light Gradient Boosting Machine (Light GBM) was employed for training vectors. Fertilization in the test dataset was assessed by MLM, with seven senior and 11 junior embryologists. Predictive metrics were analyzed using repeated measures ANOVA and paired t-tests.

Results

Comparing MLM, senior embryologists, and junior embryologists, significant differences were observed in accuracy (0.71 ± 0.01, 0.75 ± 0.05

SUBMITTER: Saito M 

PROVIDER: S-EPMC12000234 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature