Dung P. Nguyen 1,2, Quan T. Pham 2, Thanh L. Tran 1,2, Lan N. Vuong 2,3,4, Tuong M. Ho 2,3
Published: 27 October, 2021
1IVFMD PN, My Duc Phu Nhuan Hospital, Ho Chi Minh City, Vietnam
2HOPE Research Center, My Duc Hospital, Ho Chi Minh City, Vietnam
3IVFMD, My Duc Hospital, Ho Chi Minh City, Vietnam
4Department of Obstetrics and Gynecology, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam
Background: Embryo selection plays an important role in the success of in vitro fertilization (IVF). However, morphological embryo assessment has a number of limitations, including the time required, lack of accuracy, and inconsistency. This study determined whether a machine learning-based model could predict blastocyst formation using day-3 embryo images.
Methods: Day-3 embryo images from IVF/intracytoplasmic sperm injection (ICSI) cycles performed at My Duc Phu Nhuan Hospital between August 2018 and June 2019 were retrospectively analyzed to inform model development. Day-3 embryo images derived from two-pronuclear (2PN) zygotes with known blastocyst formation data were extracted from the CCM-iBIS time-lapse incubator (Astec, Japan) at 67 hours post ICSI, and labeled as blastocyst/non-blastocyst based on results at 116 hours post ICSI. Images were used as the input dataset to train (85%) and validate (15%) the convolutional neural network (CNN) model, then model accuracy was determined using the training and validation dataset. The performance of 13 experienced embryologists for predicting blastocyst formation based on 100 day-3 embryo images was also evaluated.
Results: A total of 1,135 images were allocated into training (n=”>n=n= 967) and validation (n=”>n=n= 168) sets, with an even distribution for blastocyst formation outcome. The accuracy of the final model for blastocyst formation was 97.72% in the training dataset and 76.19% in the validation dataset. The final model predicted blastocyst formation from day-3 embryo images in the validation dataset with an area under the curve of 0.75 (95% confidence interval [CI] 0.69–0.81). Embryologists predicted blastocyst formation with the accuracy of 70.07% (95% CI 68.12%–72.03%), sensitivity of 87.04% (95% CI 82.56%–91.52%), and specificity of 30.93% (95% CI 29.35%–32.51%).
Conclusions: The CNN-based machine learning model using day-3 embryo images predicted blastocyst formation more accurately than experienced embryologists. The CNN-based model is a potential tool to predict additional IVF outcomes.
Blastocyst Prediction, Machine Learning, Convolutional Neural Network