Pseudo contrastive labeling for predicting IVF embryo developmental potential

Abstract

In vitro fertilization is typically associated with high failure rates per transfer, leading to an acute need for the identification of embryos with high developmental potential. Current methods are tailored to specific times after fertilization, often require expert inspection, and have low predictive power. Automatic methods are challenged by ambiguous labels, clinical heterogeneity, and the inability to utilize multiple developmental points. In this work, we propose a novel method that trains a classifier conditioned on the time since fertilization. This classifier is then integrated over time and its output is used to assign soft labels to pairs of samples. The classifier obtained by training on these soft labels presents a significant improvement in accuracy, even as early as 30 h post-fertilization. By integrating the classification scores, the predictive power is further improved. Our results are superior to previously reported methods, including the commercial KIDScore-D3 system, and a group of eight senior professionals, in classifying multiple groups of favorable embryos into groups defined as less favorable based on implantation outcomes, expert decisions based on developmental trajectories, and/or genetic tests.

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

“…Future studies will combine both clinical variables from the donor and the patient to also account for their impact on the patient’s receptivity. There was a decrease in AUC for younger patients, as found by Erlich et al (2022) . They hypothesized that it could be due to this sub-group having more infertility due to non-embryonic causes, thus resulting in noisy labels where a high potential embryo does not lead as often to a successful pregnancy.…”

“…While it is not possible to directly compare performances with other publications that have used a different dataset to evaluate their performances, the AUCs of both video and hybrid models from this study are comparable to that of other groups that have trained their algorithms to predict the fetal heartbeat. For example, Lassen et al (2022) and Erlich et al (2022) have reported AUCs ranging from 0.621 to 0.708 on Day 5. The original study of Tran et al (2019) reported an extremely high AUC of 0.93, but only because they trained and validated their algorithms on a disproportionate number of discarded embryos; this has been acknowledged as being an easier task that facilitates higher AUCs ( Chavez-Badiola et al , 2020 ; Kan-Tor et al , 2020 ; Tran et al , 2019 , 2020a , 2020b ).…”

An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos

“…These values, while allowing a general comparison with similar AIs described in the literature (e.g. Alife Health AI ROC-AUC 0.62-0.64, Fairtility AI ROC-AUC 0.68-0.70, Vitrolife AI ROC-AUC 0.67see Loewke et al, 2022, Erlich et al, 2022, and Berntsen et al, 2022, do not give an indication of the ability of the AI model to effectively rank embryos for selection within a patient cohort during a single IVF cycle.…”

“…In recent years there has been a growing interest in the use of artificial intelligence (AI) to support embryo quality assessment, with numerous algorithms being developed for analysis of static images or time-lapse videos of embryos to aid in the selection of embryos for transfer (Berntsen et al, 2022;Chavez-Badiola et al, 2020;Erlich et al, 2022;Khosravi et al, 2019;Loewke et al, 2022;Silver et al, 2020;Tran et al, 2019;VerMilyea et al, 2020). However, due to the infancy of AI in the embryology field, few of these studies have evaluated real-world clinical use.…”

Development and validation of deep learning based embryo selection across multiple days of transfer

“…However, blastocyst culture generally results in a lower number of embryos to choose from, and for patients with poor embryo development, cleavage-stage transfers may be preferred if there is a risk of a cancelled cycle 2 . Few studies have focused on both cleavage-stage and blastocyst transfers 7,8 . Erlich et al 7 propose a combined model for handling day 3 and day 5 transfers, by predicting a score for each image in a time-lapse sequence.…”