A novel system based on artificial intelligence for predicting blastocyst viability and visualizing the explanation

Abstract

Purpose The purpose of the study was to invent and evaluate the novel artificial intelligence (AI) system named Fertility image Testing Through Embryo (FiTTE) for predicting blastocyst viability and visualizing the explanations via gradient‐based localization. Methods The authors retrospectively analyzed 19 342 static blastocyst images with related inspection histories from 9961 infertile patients who underwent in vitro fertilization. Among these data, 17 984 cycles of single‐blastocyst transfer were used for training, and data from 1358 cycles were used for testing purposes. Results The prediction accuracy for clinical pregnancy achieved by a control model using conventional Gardner scoring system was 59.8%, and area under the curve (AUC) was 0.62. FiTTE improved the prediction accuracy by using blastocyst images to 62.7% and AUC of 0.68. Additionally, the accuracy achieved by an ensemble model using image plus clinical data was 65.2% and AUC was 0.71, representing an improvement in prediction accuracy. The visualization algorithm showed brighter colors with blastocysts that resulted in clinical pregnancy. Conclusions The authors invented the novel AI system, FiTTE, which could provide more precise prediction of the probability of clinical pregnancy using blastocyst images secondary to single embryo transfer than the conventional Gardner scoring assessments. FiTTE could also provide explanation of AI prediction using colored blastocyst images.

A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems

“…The results of this study show that taking clinical parameters into account significantly increased the AUC by 6% ( Fig. 2B ), similarly to Enatsu et al (2022) who showed an increase of 4.5%. Their study was, however, applied to static images and was limited to the use of 12 clinical features, in contrast with the 31 types of variables present in the database of this study.…”

“…This is necessary to adjust the chances an embryo has to lead to a pregnancy, in the context, for example, of endometrial receptivity as it is known the interactions between the embryo and the endometrium play an important role in embryo implantation ( Lessey and Young, 2019 ). Erlich et al (2021) only used the age of the egg donor or mother, while Enatsu et al (2022) analyzed 12 clinical features (including age, anti-Müllerian hormone (AMH), endometrial thickness) but in combination with single images of the blastocyst. The aim of this study was to test whether analyzing a large subset of clinical features improved the performances of a deep learning algorithm that predicts the likelihood of pregnancy of an embryo based on its kinetics.…”

Development and evaluation of a live birth prediction model for evaluating human blastocysts: a retrospective study

“…The blastocyst grading system introduced in 1999 ( Gardner 1999, Gardner and Schoolcraft, 1999 ) remains the most common method used by embryologists to evaluate blastocyst quality although the morphological grades of blastocyst development stage, ICM and TE have limited predictive power on live birth outcomes (e.g., AUC= 0.58-0.61 for live birth prediction reported by Reignier et al, 2019; Bartolacci et al, 2021; Xiong et al, 2022 ). Since CNN became a state-of-the-art method for image-based classification, many attempts have been made to apply the CNN to blastocyst evaluation for predicting clinical outcomes (e.g., VerMilyea et al, 2020; Kragh et al, 2021; Berntsen et al, 2022; Enatsu et al, 2022; Loewke et al, 2022; Miyagi et al, 2019; Nagaya et al, 2021 ). Among these, the AUC values reported in the literature using blastocyst images only were around 0.65 for live birth prediction ( Miyagi et al, 2019; Nagaya et al, 2021 ).…”

“…The area under the receiver operating characteristic (ROC) curve (AUC) is the most commonly used metric to evaluate and compare machine learning models on predicting clinical outcomes of blastocysts ( Kragh et al, 2021 ). The AUCs reported in the literature using CNN to predict clinical outcomes from blastocyst images range from 0.64 to 0.71 for pregnancy prediction ( VerMilyea et al, 2020; Kragh et al, 2021; Berntsen et al, 2022; Enatsu et al, 2022; Loewke et al, 2022 ), and are around 0.65 for live birth prediction ( Miyagi et al, 2019, Nagaya et al, 2021 ).…”

“…These videos contain the entire development process from day 0 to day 5-7. However, results in the literature show that CNN using static images achieved similar or slightly better accuracies in predicting clinical outcomes than using time-lapse videos, for instance, AUC= 0.68-0.71 ( VerMilyea et al, 2020; Enatsu et al, 2022; Loewke et al, 2022 ) vs. 0.64-0.67 ( Kragh et al, 2021; Berntsen et al, 2022 ) for pregnancy prediction, and 0.66 ( Miyagi et al, 2019 ) vs. 0.65 ( Nagaya et al, 2021 ) for live birth prediction. A potential reason is that the redundant frames of images in time-lapse videos may work as noise causing the model to overfit and thus leading to a lower prediction accuracy ( Zhu et al, 2018; Wu et al, 2021; Tao et al, 2022 ).…”