# Pregnancy AI: Development and Internal Validation of an Artificial Intelligence Tool to Predict Live Births in ICSI and IVF Cycles Using Clinical Features and Embryo Images

## TL;DR

This study developed an AI tool combining clinical data and embryo images to predict live births in IVF and ICSI treatments, showing improved accuracy when both data types are used.

## Contribution

The novel contribution is combining SVM and CNN models using clinical features and embryo time-lapse images to predict reproductive outcomes.

## Key findings

- The model predicted transferrable embryos with 0.98 accuracy using clinical data.
- Combining clinical data and embryo images improved live birth prediction accuracy to 0.71.
- Using only embryo images, the model achieved 0.72 accuracy for predicting biochemical pregnancy.

## Abstract

Background and Objectives: This study aimed at developing an AI-based predictive model for live birth based on a combination of a support vector machine (SVM) using clinical and embryological features, together with a convolutional neural network (CNN) using embryo time-lapse videos. Materials and Methods: This was a retrospective cohort analysis. Two hundred fifty-nine infertile couples treated between January 2012 and December 2019, with a total of 2330 embryos, were included in this study, and clinical data and images from 355 transferred embryos were used to build a predictive model. The main outcome was accuracy of live birth prediction. The secondary outcomes included accuracy in the prediction of biochemical pregnancy, clinical pregnancy and transferrable embryos. Results: The model was able to predict the transferrable embryo (i.e., embryos suitable for transfer or cryopreservation) with an accuracy of 0.98 in an internal set. The accuracy for predicting live birth, clinical pregnancy, and biochemical pregnancy exclusively using clinical data as input for an SVM model was 0.67, 0.68, and 0.67, respectively. With six frames from time-lapse embryo development, the CNN produced an accuracy of 0.57, 0.67, and 0.72. The predictive model performed best when combining input from clinical data and images from multiple embryo developmental frames, obtaining 0.71, 0.73, and 0.77 for predicting live birth, clinical pregnancy, and biochemical pregnancy. Conclusions: This study highlights the potential of combining clinical data and embryo development images to enhance predictive models in reproductive medicine.