Artificial intelligence-powered assisted ranking of sibling embryos to increase first cycle pregnancy rate

RESEARCH QUESTION

Could EMBRYOLY, an artificial intelligence embryo evaluation tool, assist embryologists to increase first cycle pregnancy rate and reduce cycles to pregnancy for patients?

DESIGN

Data from 11,988 embryos were collected via EMBRYOLY from 2666 egg retrievals (2019-2022) across 11 centres in France, Spain and Morocco using three time-lapse systems (TLS). Data from two independent clinics were also examined. EMBRYOLY's transformer-based model was applied to transferred embryos to evaluate ranking performances against pregnancy and birth outcomes. It was applied to cohorts to rank sibling embryos (including non-transferred) according to their likelihood of clinical pregnancy and to compute the agreement with the embryologist's highest ranked embryo. Its effect on time to pregnancy and first cycle pregnancy rate was evaluated on cohorts with multiple single blastocyst transfers, assuming the embryologist would have considered EMBRYOLY's ranking on the embryos favoured for transfer.

RESULTS

EMBRYOLY's score correlated significantly with clinical pregnancies and live births for cleavage and blastocyst transfers. This held true for clinical pregnancies from blastocyst transfers in two independent clinics. In cases of multiple single embryo transfers, embryologists achieved a 19.8% first cycle pregnancy rate, which could have been improved to 44.1% with the adjunctive use of EMBRYOLY (McNemar's test: P < 0.001). This could have reduced cycles to clinical pregnancy from 2.01 to 1.66 (Wilcoxon test: P < 0.001).

CONCLUSIONS

EMBRYOLY's potential to enhance first cycle pregnancy rates when combined with embryologists' expertise is highlighted. It reduces the number of unsuccessful cycles for patients across TLS and IVF centres.

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

STUDY QUESTION

Can artificial intelligence (AI) algorithms developed to assist embryologists in evaluating embryo morphokinetics be enriched with multi-centric clinical data to better predict clinical pregnancy outcome?

SUMMARY ANSWER

Training algorithms on multi-centric clinical data significantly increased AUC compared to algorithms that only analyzed the time-lapse system (TLS) videos.

WHAT IS KNOWN ALREADY

Several AI-based algorithms have been developed to predict pregnancy, most of them based only on analysis of the time-lapse recording of embryo development. It remains unclear, however, whether considering numerous clinical features can improve the predictive performances of time-lapse based embryo evaluation.

STUDY DESIGN, SIZE, DURATION

A dataset of 9986 embryos (95.60% known clinical pregnancy outcome, 32.47% frozen transfers) from 5226 patients from 14 European fertility centers (in two countries) recorded with three different TLS was used to train and validate the algorithms. A total of 31 clinical factors were collected. A separate test set (447 videos) was used to compare performances between embryologists and the algorithm.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Clinical pregnancy (defined as a pregnancy leading to a fetal heartbeat) outcome was first predicted using a 3D convolutional neural network that analyzed videos of the embryonic development up to 2 or 3 days of development (33% of the database) or up to 5 or 6 days of development (67% of the database). The output video score was then fed as input alongside clinical features to a gradient boosting algorithm that generated a second score corresponding to the hybrid model. AUC was computed across 7-fold of the validation dataset for both models. These predictions were compared to those of 13 senior embryologists made on the test dataset.

MAIN RESULTS AND THE ROLE OF CHANCE

The average AUC of the hybrid model across all 7-fold was significantly higher than that of the video model (0.727 versus 0.684, respectively, P = 0.015; Wilcoxon test). A SHapley Additive exPlanations (SHAP) analysis of the hybrid model showed that the six first most important features to predict pregnancy were morphokinetics of the embryo (video score), oocyte age, total gonadotrophin dose intake, number of embryos generated, number of oocytes retrieved, and endometrium thickness. The hybrid model was shown to be superior to embryologists with respect to different metrics, including the balanced accuracy (P ≤ 0.003; Wilcoxon test). The likelihood of pregnancy was linearly linked to the hybrid score, with increasing odds ratio (maximum P-value = 0.001), demonstrating the ranking capacity of the model. Training individual hybrid models did not improve predictive performance. A clinic hold-out experiment was conducted and resulted in AUCs ranging between 0.63 and 0.73. Performance of the hybrid model did not vary between TLS or between subgroups of embryos transferred at different days of embryonic development. The hybrid model did fare better for patients older than 35 years (P < 0.001; Mann-Whitney test), and for fresh transfers (P < 0.001; Mann-Whitney test).

LIMITATIONS, REASONS FOR CAUTION

Participant centers were located in two countries, thus limiting the generalization of our conclusion to wider subpopulations of patients. Not all clinical features were available for all embryos, thus limiting the performances of the hybrid model in some instances.

WIDER IMPLICATIONS OF THE FINDINGS

Our study suggests that considering clinical data improves pregnancy predictive performances and that there is no need to retrain algorithms at the clinic level unless they follow strikingly different practices. This study characterizes a versatile AI algorithm with similar performance on different time-lapse microscopes and on embryos transferred at different development stages. It can also help with patients of different ages and protocols used but with varying performances, presumably because the task of predicting fetal heartbeat becomes more or less hard depending on the clinical context. This AI model can be made widely available and can help embryologists in a wide range of clinical scenarios to standardize their practices.

STUDY FUNDING/COMPETING INTEREST(S)

Funding for the study was provided by ImVitro with grant funding received in part from BPIFrance (Bourse French Tech Emergence (DOS0106572/00), Paris Innovation Amorçage (DOS0132841/00), and Aide au Développement DeepTech (DOS0152872/00)). A.B.-C. is a co-owner of, and holds stocks in, ImVitro SAS. A.B.-C. and F.D.M. hold a patent for 'Devices and processes for machine learning prediction of in vitro fertilization' (EP20305914.2). A.D., N.D., M.M.F., and F.D.M. are or have been employees of ImVitro and have been granted stock options. X.P.-V. has been paid as a consultant to ImVitro and has been granted stocks options of ImVitro. L.C.-D. and C.G.-S. have undertaken paid consultancy for ImVitro SAS. The remaining authors have no conflicts to declare.

TRIAL REGISTRATION NUMBER

N/A.

Artificial intelligence in human in vitro fertilization and embryology

Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the "best" embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.

A Preclinical Evaluation towards the Clinical Application of Oxygen Consumption Measurement by CERMs by a Mouse Chimera Model

We have developed an automated device for the measurement of oxygen consumption rate (OCR) called Chip-sensing Embryo Respiratory Measurement system (CERMs). To verify the safety and the significance of the OCR measurement by CERMs, we conducted comprehensive tests using a mouse model prior to clinical trials in a human in vitro fertilization (IVF) program. Embryo transfer revealed that the OCR measured by CERMs did not compromise the full-term development of mice or their future fertility, and was positively correlated with adenosine triphosphate (ATP) production and the mitochondrial membrane potential (ΔΨm), thereby indirectly reflecting mitochondrial oxidative phosphorylation (OXPHOS) activity. We demonstrated that the OCR is independent of embryo morphology (the size) and number of mitochondria (mitochondrial DNA copy number). The OCR correlated with the total cell numbers, whereas the inner cell mass (ICM) cell numbers and the fetal developmental rate were not. Thus, the OCR may serve as an indicator of the numbers of trophectoderm (TE) cells, rather than number or quality of ICM cells. However, implantation ability was neither correlated with the OCR, nor the embryo size in this model. This can probably be attributed to the limitation that chimeric embryos contain non-physiological high TE cells counts that are beneficial for implantation. CERMs can be safely employed in clinical IVF owing to it being a safe, highly effective, non-invasive, accurate, and quantitative tool for OCR measurement. Utilization of CERMs for clinical testing of human embryos would provide further insights into the nature of oxidative metabolism and embryonic viability.

External validation of a time-lapse model; a retrospective study comparing embryo evaluation using a morphokinetic model to standard morphology with live birth as endpoint

Objective

To validate a morphokinetic implantation model developed for EmbryoScope on embryos with known outcome, compared to standard morphology in a retrospective single center study.

Methods

Morphokinetic annotation of 768 embryos with known outcome between 2013 -2015; corresponding to 116 D3 fresh embryos, 80 D6 frozen blastocysts, and 572 D5 blastocysts, fresh or frozen. The embryos were ranked by the KIDScore into five classes, KID1-5, and grouped into four classes based on standard morphology. Pregnancy rates, clinical pregnancy rates and live birth rates were compared. Combinations of morphology and morphokinetics were evaluated for implantation rates and live births.

Results

Live birth rate increased with increasing KIDScore, from 19% for KID1 to 42% for KID5. Of all live births, KID5 contributed with 71%, KID4 with 20%, KID3 with 4%, KID2 with 4%, and KID1 with 2%. For morphology, the corresponding figure was 43% for Top Quality, 47% for Good Quality, 4% for Poor Quality, and 5% for Slow embryos. For day 3 embryos, KID5 embryos had the highest live birth rates, and contributed to 83% of the live births; whereas the second best morphological class had the highest live birth rate and contributed to most of the live births. For blastocysts, the KIDScore and morphology performed equally well. Combining morphology and morphokinetics indicated stronger predictive power for morphokinetics.

Conclusions

Overall, the KIDScore correlates with both implantation and live birth in our clinical setting. Compared to morphology, the KIDScore was superior for day 3 embryos, and equally good for blastocysts at predicting live births.