Time-lapse algorithms and morphological selection of day-5 embryos for transfer: a preclinical validation study

Developing and validating a prediction model of live birth following single vitrified-warmed blastocyst transfer

RESEARCH QUESTION

Can the developed clinical prediction model offer an accurate estimate of the likelihood of live birth, involving blastocyst morphology and vitrification day after single vitrified-warmed blastocyst transfer (SVBT), and therefore assist clinicians and patients?

STUDY DESIGN

Retrospective cohort study conducted at a Spanish university-based reproductive medicine unit (2017-2021) including consecutive vitrified-warmed blastocysts from IVF cycles. A multivariable logistic regression incorporated key live birth predictors: vitrification day, embryo score, embryo ploidy status and clinically relevant variables, i.e. maternal age.

RESULTS

The training set involved 1653 SVBT cycles carried out between 2017 and 2020; 592 SVBT cycles from 2021 constituted the external validation dataset. The model revealed that female age and embryo characteristics, including overall quality and blastulation day, is linked to live birth rate in SVBT cycles. Stratification by vitrification day and quality (from day-5A to day-6 C blastocysts) applied to genetically tested and untested embryos. The model's area under the curve was 0.66 (95% CI 0.64 to 0.69) during development and 0.65 (95% CI 0.61 to 0.70) in validation, denoting moderate discrimination. Calibration plots showed strong agreement between predicted and observed probabilities.

CONCLUSION

By incorporating essential predictors such as vitrification day, embryo morphology grade, age and preimplantation genetic testing for aneuploidy usage, this predictive model offers valuable guidance to clinicians and patients, enabling accurate forecasts of live birth rates for any given vitrified blastocyst within SVBT cycles. Additionally, it serves as a potentially indispensable laboratory tool, aiding in selecting the most promising blastocysts for optimal outcomes.

A comparison of morphokinetic models and morphological selection for prioritizing euploid embryos: a multicentre cohort study

STUDY QUESTION

Are morphokinetic models better at prioritizing a euploid embryo for transfer over morphological selection by an embryologist?

SUMMARY ANSWER

Morphokinetic algorithms lead to an improved prioritization of euploid embryos when compared to embryologist selection.

WHAT IS KNOWN ALREADY

PREFER (predicting euploidy for embryos in reproductive medicine) is a previously published morphokinetic model associated with live birth and miscarriage. The second model uses live birth as the target outcome (LB model).

STUDY DESIGN, SIZE, DURATION

Data for this cohort study were obtained from 1958 biopsied blastocysts at nine IVF clinics across the UK from January 2021 to December 2022.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The ability of the PREFER and LB models to prioritize a euploid embryo was compared against arbitrary selection and the prediction of four embryologists using the timelapse video, blinded to the morphokinetic time stamp. The comparisons were made using calculated percentages and normalized discounted cumulative gain (NDCG), whereby an NDCG score of 1 would equate to all euploid embryos being ranked first. In arbitrary selection, the ploidy status was randomly assigned within each cycle and the NDGC calculated, and this was then repeated 100 times and the mean obtained.

MAIN RESULTS AND THE ROLE OF CHANCE

Arbitrary embryo selection would rank a euploid embryo first 37% of the time, embryologist selection 39%, and the LB and PREFER ploidy morphokinetic models 46% and 47% of the time, respectively. The AUC for LB and PREFER model was 0.62 and 0.63, respectively. Morphological selection did not significantly improve the performance of both morphokinetic models when used in combination. There was a significant difference between the NDGC metric of the PREFER model versus embryologist selection at 0.96 and 0.87, respectively (t = 14.1, P < 0.001). Similarly, there was a significant difference between the LB model and embryologist selection with an NDGC metric of 0.95 and 0.87, respectively (t = 12.0, P < 0.001). All four embryologists ranked embryos similarly, with an intraclass coefficient of 0.91 (95% CI 0.82-0.95, P < 0.001).

LIMITATIONS, REASONS FOR CAUTION

Aside from the retrospective study design, limitations include allowing the embryologist to watch the time lapse video, potentially providing more information than a truly static morphological assessment. Furthermore, the embryologists at the participating centres were familiar with the significant variables in time lapse, which could bias the results.

WIDER IMPLICATIONS OF THE FINDINGS

The present study shows that the use of morphokinetic models, namely PREFER and LB, translates into improved euploid embryo selection.

STUDY FUNDING/COMPETING INTEREST(S)

This study received no specific grant funding from any funding agency in the public, commercial or not-for-profit sectors. Dr Alison Campbell is minor share holder of Care Fertility. All other authors have no conflicts of interest to declare. Time lapse is a technology for which patients are charged extra at participating centres.

TRIAL REGISTRATION NUMBER

N/A.

The effect of embryo selection using time-lapse monitoring on IVF/ICSI outcomes: A systematic review and meta-analysis

AIM

To explore the effect of embryo selection using the time-lapse monitoring (TLM) system compared with conventional morphological selection (CMS) on in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) outcomes.

METHODS

We searched PubMed, Ovid-Embase, and The Cochrane Library for the following studies: At Comparison 1, embryo selection using TLM images in a TLM incubator based on morphology versus embryo selection using CMS in a conventional incubator based on morphology; at Comparison 2, embryo selection using TLM based on morphokinetics versus embryo selection using CMS based on morphology. The primary outcomes were the live birth rate (LBR), ongoing pregnancy rate (OPR), clinical pregnancy rate (CPR), and implantation rate (IR), and the secondary outcome was the miscarriage rate (MR).

RESULTS

A total of 14 randomized control trials (RCTs) were included. Both based on morphology, TLM incubators increased the IR (risk ratio [RR]: 1.10; 95% confidence interval [CI]: 1.01, 1.18; I2  = 0%, moderate-quality evidence) compared to conventional incubators. Low- to moderate-quality evidence suggests that TLM incubators did not improve LBR, OPR, CPR, and MR compared to conventional incubators. In addition, low- to moderate-quality evidence indicates that embryo selection using TLM based on morphokinetics did not improve LBR, OPR, CPR, IR, or MR compared to CMS based on morphology.

CONCLUSIONS

Low- to moderate-quality evidence suggests that neither TLM incubators nor embryo selection using TLM based on morphokinetics improved clinical outcomes (LBR, OPR, CPR, and MR) compared with CMS based on morphology. TLM is still an investigational procedure for IVF/ICSI practice.

Short-interval second ejaculation improves sperm quality, blastocyst formation in oligoasthenozoospermic males in ICSI cycles: a time-lapse sibling oocytes study

Background

Does short-interval second ejaculation improve sperm quality, embryo development and clinical outcomes for oligoasthenozoospermia males received intracytoplasmic sperm injection (ICSI) treatment?

Methods

All enrolled male patients underwent short-interval secondary ejaculation on the day of oocyte retrieval, and 786 sibling MII oocytes from 67 cycles were equally divided into two groups based on whether the injected spermatozoons originated from the first or second ejaculation. Semen parameters, embryo development efficiency, morphokinetic parameters and clinical outcomes were compared between the two groups to assess the efficiency and clinical value of short-interval second ejaculation in ICSI cycles.

Results

Short-interval second ejaculation significantly improved sperm motility, normal morphological rate, and sperm DNA integrity both before and after sperm swim-up. The high-quality blastocyst rate (24.79% versus 14.67%), available blastocyst rate (57.56% versus 48.44%), and oocyte utilization rate (52.93% versus 45.29%) were significantly higher in the second ejaculation group (P<0.05). The clinical pregnancy rate (59.09% versus 47.37%), implantation rate (42.11% versus 32.35%) and live birth rate (40.91% versus 31.58%) were higher in the second ejaculation group, but the differences were not significant (P>0.05). Time-lapse analysis showed that morphokinetic time points after the 7-cell stage were earlier in the second ejaculation group but without a significant difference (P>0.05), and abnormal embryo cleavage patterns between the two groups were not significantly different (P>0.05).

Conclusions

Short-interval second ejaculation significantly improves sperm quality in oligoasthenozoospermic males, and is beneficial for blastocyst formation efficiency in ICSI cycles. This study suggested a non-invasive and simple but effective strategy for improving ICSI treatment outcomes.

Using the embryo-uterus statistical model to predict pregnancy chances by using cleavage stage morphokinetics and female age: two centre-specific prediction models and mutual validation

BACKGROUND

The predictive capability of time-lapse monitoring (TLM) selection algorithms is influenced by patient characteristics, type and quality of data included in the analysis and the used statistical methods. Previous studies excluded DET cycles of which only one embryo implanted, introducing bias into the data. Therefore, we wanted to develop a TLM prediction model that is able to predict pregnancy chances after both single- and double embryo transfer (SET and DET).

METHODS

This is a retrospective study of couples (n = 1770) undergoing an in vitro fertilization cycle at the Erasmus MC, University Medical Centre Rotterdam (clinic A) or the Reinier de Graaf Hospital (clinic B). This resulted in 2058 transferred embryos with time-lapse and pregnancy outcome information. For each dataset a prediction model was established by using the Embryo-Uterus statistical model with the number of gestational sacs as the outcome variable. This process was followed by cross-validation.

RESULTS

Prediction model A (based on data of clinic A) included female age, t3-t2 and t5-t4, and model B (clinic B) included female age, t2, t3-t2 and t5-t4. Internal validation showed overfitting of model A (calibration slope 0.765 and area under the curve (AUC) 0.60), and minor overfitting of model B (slope 0.915 and AUC 0.65). External validation showed that model A was capable of predicting pregnancy in the dataset of clinic B with an AUC of 0.65 (95% CI: 0.61-0.69; slope 1.223, 95% CI: 0.903-1.561). Model B was less accurate in predicting pregnancy in the dataset of clinic A (AUC 0.60, 95% CI: 0.56-0.65; slope 0.671, 95% CI: 0.422-0.939).

CONCLUSION

Our study demonstrates a novel approach to the development of a TLM prediction model by applying the EU statistical model. With further development and validation in clinical practice, our prediction model approach can aid in embryo selection and decision making for SET or DET.

Development of an artificial intelligence based model for predicting the euploidy of blastocysts in PGT-A treatments

The euploidy of embryos is unpredictable before transfer in in vitro fertilisation (IVF) treatments without pre-implantation genetic testing (PGT). Previous studies have suggested that morphokinetic characteristics using an artificial intelligence (AI)-based model in the time-lapse monitoring (TLM) system were correlated with the outcomes of frozen embryo transfer (FET), but the predictive effectiveness of the model for euploidy remains to be perfected. In this study, we combined morphokinetic characteristics, morphological characteristics of blastocysts, and clinical parameters of patients to build a model to predict the euploidy of blastocysts and live births in PGT for aneuploidy treatments. The model was effective in predicting euploidy (AUC = 0.879) but was ineffective in predicting live birth after FET. These results provide a potential method for the selection of embryos for IVF treatments with non-PGT.

Detailed analysis of cytoplasmic strings in human blastocysts: new insights

The aim of this study was to determine if there was an association between the presence of cytoplasmic strings (CS) and their characteristics, with blastocyst quality, development and clinical outcome in human blastocysts. This two-centre cohort study was performed between July 2017 and September 2018 and involved a total of 1152 blastocysts from 225 patients undergoing in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI). All embryos were cultured in Embryoscope+ and were assessed for CS using time-lapse images. A single assessor examined all blastocysts and reviewed videos using the EmbyroViewer® Software. Blastocyst quality was assessed on day 5 of embryo development. The number of CS, location and duration of their activity was recorded on days 5/6. A positive association between the presence of CS in human blastocysts with blastocyst quality was identified. Blastocysts with a higher number of CS present, were of higher quality and were in the more advanced stages of development. Top quality blastocysts had CS activity present for longer, as well as having a higher number of vesicles present travelling along the CS. Blastocysts that had CS present, had a significantly higher live birth rate. This study has confirmed that a higher number of CS and vesicles in human blastocysts is associated with top quality blastocysts and is not a negative predictor of development. They had a higher number of CS present that appeared earlier in development and, although ceased activity sooner, had a longer duration of activity. Blastocysts with CS had a significant increase in live birth rate.

Quantitative approaches in clinical reproductive endocrinology

Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.

Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity?

STUDY QUESTION

What is the accuracy and agreement of embryologists when assessing the implantation probability of blastocysts using time-lapse imaging (TLI), and can it be improved with a data-driven algorithm?

SUMMARY ANSWER

The overall interobserver agreement of a large panel of embryologists was moderate and prediction accuracy was modest, while the purpose-built artificial intelligence model generally resulted in higher performance metrics.

WHAT IS KNOWN ALREADY

Previous studies have demonstrated significant interobserver variability amongst embryologists when assessing embryo quality. However, data concerning embryologists' ability to predict implantation probability using TLI is still lacking. Emerging technologies based on data-driven tools have shown great promise for improving embryo selection and predicting clinical outcomes.

STUDY DESIGN, SIZE, DURATION

TLI video files of 136 embryos with known implantation data were retrospectively collected from two clinical sites between 2018 and 2019 for the performance assessment of 36 embryologists and comparison with a deep neural network (DNN).

PARTICIPANTS/MATERIALS, SETTING, METHODS

We recruited 39 embryologists from 13 different countries. All participants were blinded to clinical outcomes. A total of 136 TLI videos of embryos that reached the blastocyst stage were used for this experiment. Each embryo's likelihood of successfully implanting was assessed by 36 embryologists, providing implantation probability grades (IPGs) from 1 to 5, where 1 indicates a very low likelihood of implantation and 5 indicates a very high likelihood. Subsequently, three embryologists with over 5 years of experience provided Gardner scores. All 136 blastocysts were categorized into three quality groups based on their Gardner scores. Embryologist predictions were then converted into predictions of implantation (IPG ≥ 3) and no implantation (IPG ≤ 2). Embryologists' performance and agreement were assessed using Fleiss kappa coefficient. A 10-fold cross-validation DNN was developed to provide IPGs for TLI video files. The model's performance was compared to that of the embryologists.

MAIN RESULTS AND THE ROLE OF CHANCE

Logistic regression was employed for the following confounding variables: country of residence, academic level, embryo scoring system, log years of experience and experience using TLI. None were found to have a statistically significant impact on embryologist performance at α = 0.05. The average implantation prediction accuracy for the embryologists was 51.9% for all embryos (N = 136). The average accuracy of the embryologists when assessing top quality and poor quality embryos (according to the Gardner score categorizations) was 57.5% and 57.4%, respectively, and 44.6% for fair quality embryos. Overall interobserver agreement was moderate (κ = 0.56, N = 136). The best agreement was achieved in the poor + top quality group (κ = 0.65, N = 77), while the agreement in the fair quality group was lower (κ = 0.25, N = 59). The DNN showed an overall accuracy rate of 62.5%, with accuracies of 62.2%, 61% and 65.6% for the poor, fair and top quality groups, respectively. The AUC for the DNN was higher than that of the embryologists overall (0.70 DNN vs 0.61 embryologists) as well as in all of the Gardner groups (DNN vs embryologists-Poor: 0.69 vs 0.62; Fair: 0.67 vs 0.53; Top: 0.77 vs 0.54).

LIMITATIONS, REASONS FOR CAUTION

Blastocyst assessment was performed using video files acquired from time-lapse incubators, where each video contained data from a single focal plane. Clinical data regarding the underlying cause of infertility and endometrial thickness before the transfer was not available, yet may explain implantation failure and lower accuracy of IPGs. Implantation was defined as the presence of a gestational sac, whereas the detection of fetal heartbeat is a more robust marker of embryo viability. The raw data were anonymized to the extent that it was not possible to quantify the number of unique patients and cycles included in the study, potentially masking the effect of bias from a limited patient pool. Furthermore, the lack of demographic data makes it difficult to draw conclusions on how representative the dataset was of the wider population. Finally, embryologists were required to assess the implantation potential, not embryo quality. Although this is not the traditional approach to embryo evaluation, morphology/morphokinetics as a means of assessing embryo quality is believed to be strongly correlated with viability and, for some methods, implantation potential.

WIDER IMPLICATIONS OF THE FINDINGS

Embryo selection is a key element in IVF success and continues to be a challenge. Improving the predictive ability could assist in optimizing implantation success rates and other clinical outcomes and could minimize the financial and emotional burden on the patient. This study demonstrates moderate agreement rates between embryologists, likely due to the subjective nature of embryo assessment. In particular, we found that average embryologist accuracy and agreement were significantly lower for fair quality embryos when compared with that for top and poor quality embryos. Using data-driven algorithms as an assistive tool may help IVF professionals increase success rates and promote much needed standardization in the IVF clinic. Our results indicate a need for further research regarding technological advancement in this field.

STUDY FUNDING/COMPETING INTEREST(S)

Embryonics Ltd is an Israel-based company. Funding for the study was partially provided by the Israeli Innovation Authority, grant #74556.

TRIAL REGISTRATION NUMBER

N/A.

Faster Fertilization and Cleavage Kinetics Reflect Competence to Achieve a Live Birth: Data from Single-Embryo Transfer Cycles

The aim of this study was to assess the relationship between early developmental kinetics and the competence to result in a live birth as well as the impact of maternal age and the number of retrieved oocytes. This retrospective cohort study included 3,021 single-embryo transfer cycles and assessed live birth outcomes paired with morphokinetic data; 1,412 transfers resulted in live births (LB), and 1,609 did not (NLB). Early morphokinetic parameters between LB and NLB embryos were compared from patients stratified into four age groups (20-25, 26-30, 31-36, and ≥37 years) and between embryos in the same competence groups within the age groups. Early morphokinetic parameters were also compared between LB and NLB embryos from patients stratified into four groups based on the number of oocytes harvested (≤7, 8-14, 15-21, and ≥22). The association between morphokinetic parameters and LB was tested using univariate and multivariate analyses. This study indicated that embryos resulting in LB generally exhibit faster developmental dynamic parameters than embryos that do not. However, this difference decreased in the younger (20-25 years) and older (≥37 years) age groups. In addition, when the number of harvested oocytes was low (≤7) or high (≥22), this difference was less obvious. The morphokinetic parameters of embryonic cleavage are an effective reference value for embryo selection strategies aimed at increasing live birth rates, especially for patients aged 26–36 years, with 8–21 harvested oocytes.

Can Time-Lapse Incubation and Monitoring Be Beneficial to Assisted Reproduction Technology Outcomes? A Randomized Controlled Trial Using Day 3 Double Embryo Transfer

Objective: To determine if the application of time-lapse incubation and monitoring can be beneficial to clinical outcomes in assisted reproductive technology.

Methods: A total of 600 patients were equally randomized to three groups, namely, conventional embryo culture and standard morphological selection (CM group), time-lapse culture and standard morphological selection (TLM group), and time-lapse culture and morphokinetic selection (TLA group). Notably, 424 undergoing fresh autologous in vitro fertilization cycles were analyzed, 132 patients in the CM group, 158 in the TLM group, and 134 in the TLA group. Main outcomes included clinical outcomes, embryo development rates, and perinatal outcomes.

Results: Clinical pregnancy rates in the time-lapse groups were significantly higher than in the CM group (CM 65.2% vs. TLM 77.2% vs. TLA 81.3%). Implantation rates and live birth rates were significantly higher for the TLA group (59.7 and 70.9%) compared with the CM group (47.7 and 56.1%) but not compared with the TLM group (55.4 and 67.1%). There was no statistical difference in miscarriage and ectopic pregnancy rates among the three groups. Overall, birth weight was significantly higher in the time-lapse groups (CM 2,731.7 ± 644.8 g vs. TLM 3,066.5 ± 595.4 g vs. TLA 2,967.4 ± 590.0 g). The birth height of newborns in the TLM group was significantly longer than that of the CM group and TLA group (CM 48.3± 4.4 cm vs. TLM 49.8± 2.3 cm vs. TLA 48.5± 2.7 cm).

Conclusion: Time-lapse incubation and monitoring have a significant benefit on clinical pregnancy rates and on overall birth weights while morphokinetic analysis is not necessary.

Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [NCT02974517].

Time-Lapse Systems: A Comprehensive Analysis on Effectiveness

Time-lapse systems have quickly become a common feature of in vitro fertilization laboratories all over the world. Since being introduced over a decade ago, the alleged benefits of time-lapse technology have continued to grow, from undisturbed culture conditions and round the clock, noninvasive observations to more recent computer-assisted selection of embryos through the development of algorithms. Despite the global uptake of time-lapse technology, its real impact on clinical outcomes is still controversial. This review aims to explore the different features offered by time-lapse technology, discussing incubation, algorithms, artificial intelligence and the regulation of nonessential treatment interventions, while assessing evidence on whether any benefit is offered over conventional technology.

OUP accepted manuscript

STUDY QUESTION

Can use of a commercially available time-lapse algorithm for Day 5 blastocyst selection improve pregnancy rates compared with morphology alone?

SUMMARY ANSWER

The use of a time-lapse selection model to choose blastocysts for fresh single embryo transfer on Day 5 did not improve ongoing pregnancy rate compared to morphology alone.

WHAT IS KNOWN ALREADY

Evidence from time-lapse monitoring suggests correlations between timing of key developmental events and embryo viability. No good quality evidence exists to support improved pregnancy rates following time-lapse selection.

STUDY DESIGN, SIZE, DURATION

A prospective multicenter randomized controlled trial including 776 randomized patients was performed between 2018 and 2021. Patients with at least two good quality blastocysts on Day 5 were allocated by a computer randomization program in a proportion of 1:1 into either the control group, whereby single blastocysts were selected for transfer by morphology alone, or the intervention group whereby final selection was decided by a commercially available time-lapse model. The embryologists at the time of blastocyst morphological scoring were blinded to which study group the patients would be randomized, and the physician and patients were blind to which group they were allocated until after the primary outcome was known. The primary outcome was number of ongoing pregnancies in the two groups.

PARTICIPANTS/MATERIALS, SETTING, METHODS

From 10 Nordic IVF clinics, 776 patients with a minimum of two good quality blastocysts on Day 5 (D5) were randomized into one of the two study groups. A commercial time-lapse model decided the final selection of blastocysts for 387 patients in the intervention (time-lapse) group, and blastocysts with the highest morphological score were transferred for 389 patients in the control group. Only single embryo transfers in fresh cycles were performed.

MAIN RESULTS AND THE ROLE OF CHANCE

In the full analysis set, the ongoing pregnancy rate for the time-lapse group was 47.4% (175/369) and 48.1% (181/376) in the control group. No statistically significant difference was found between the two groups: mean difference −0.7% (95% CI −8.2, 6.7, P = 0.90). Pregnancy rate (60.2% versus 59.0%, mean difference 1.1%, 95% CI −6.2, 8.4, P = 0.81) and early pregnancy loss (21.2% versus 18.5%, mean difference 2.7%, 95% CI −5.2, 10.6, P = 0.55) were the same for the time-lapse and the control group. Subgroup analyses showed that patient and treatment characteristics did not significantly affect the commercial time-lapse model D5 performance. In the time-lapse group, the choice of best blastocyst changed on 42% of occasions (154/369, 95% CI 36.9, 47.2) after the algorithm was applied, and this rate was similar for most treatment clinics.

LIMITATIONS, REASONS FOR CAUTION

During 2020, the patient recruitment rate slowed down at participating clinics owing to coronavirus disease-19 restrictions, so the target sample size was not achieved as planned and it was decided to stop the trial prematurely. The study only investigated embryo selection at the blastocyst stage on D5 in fresh IVF transfer cycles. In addition, only blastocysts of good morphological quality were considered for transfer, limiting the number of embryos for selection in both groups: also, it could be argued that this manual preselection of blastocysts limits the theoretical selection power of time-lapse, as well as restricting the results mainly to a good prognosis patient group. Most patients were aimed for blastocyst stage transfer when a minimum of five zygotes were available for extended culture. Finally, the primary clinical outcome evaluated was pregnancy to only 6–8 weeks.

WIDER IMPLICATIONS OF THE FINDINGS

The study suggests that time-lapse selection with a commercially available time-lapse model does not increase chance of ongoing pregnancy after single blastocyst transfer on Day 5 compared to morphology alone.

STUDY FUNDING/COMPETING INTEREST(S)

The study was financed by a grant from the Swedish state under the ALF-agreement between the Swedish government and the county councils (ALFGBG-723141). Vitrolife supported the study with embryo culture dishes and culture media. During the study period, T.H. changed his employment from Livio AB to Vitrolife AB. All other authors have no conflicts of interests to disclose.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov registration number NCT03445923.

TRIAL REGISTRATION DATE

26 February 2018.

DATE OF FIRST PATIENT’S ENROLMENT

11 June 2018.

Change in the Strategy of Embryo Selection with Time-Lapse System Implementation-Impact on Clinical Pregnancy Rates

Time-lapse systems (TLS) and associated algorithms are interesting tools to improve embryo selection. This study aimed to evaluate how TLS and KIDScore™ algorithm changed our practices of embryo selection, as compared to a conventional morphological evaluation, and improved clinical pregnancy rates (CPR). In the study group (year 2020, n = 303 transfers), embryos were cultured in an EmbryoScope+ time-lapse incubator. A first team observed embryos conventionally once a day, while a second team selected the embryos for transfer based on time-lapse recordings. In the control group (year 2019, n = 279 transfers), embryos were selected using the conventional method, and CPR were recorded. In 2020, disagreement between TLS and the conventional method occurred in 32.1% of transfers, more often for early embryos (34.7%) than for blastocysts (20.5%). Irregular morphokinetic events (direct or reverse cleavage, multinucleation, abnormal pronuclei) were detected in 54.9% of the discordant embryos. When it was available, KIDScore™ was decreased for 73.2% of the deselected embryos. Discordant blastocysts mainly corresponded with a decrease in KIDScore™ (90.9%), whereas discordant Day 3 embryos resulted from a decreased KIDScore™ and/or an irregular morphokinetic event. CPR was significantly improved in the TLS group (2020), as compared to the conventional group (2019) (32.3% vs. 21.9%, p = 0.005), even after multivariate analysis. In conclusion, TLS is useful to highlight some embryo development abnormalities and identify embryos with the highest potential for pregnancy.

Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients

Recurrent reproductive failure (RRF), such as recurrent pregnancy loss and repeated implantation failure, is characterized by complex etiologies and particularly associated with diverse maternal factors. It is currently believed that RRF is closely associated with the maternal environment, which is, in turn, affected by complex immune factors. Without the use of automated tools, it is often difficult to assess the interaction and synergistic effects of the various immune factors on the pregnancy outcome. As a result, the application of Artificial Intelligence (A.I.) has been explored in the field of assisted reproductive technology (ART). In this study, we reviewed studies on the use of A.I. to develop prediction models for pregnancy outcomes of patients who underwent ART treatment. A limited amount of models based on genetic markers or common indices have been established for prediction of pregnancy outcome of patients with RRF. In this study, we applied A.I. to analyze the medical information of patients with RRF, including immune indicators. The entire clinical samples set (561 samples) was divided into two sets: 90% of the set was used for training and 10% for testing. Different data panels were established to predict pregnancy outcomes at four different gestational nodes, including biochemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth, respectively. The prediction models of pregnancy outcomes were established using sparse coding, based on six data panels: basic patient characteristics, hormone levels, autoantibodies, peripheral immunology, endometrial immunology, and embryo parameters. The six data panels covered 64 variables. In terms of biochemical pregnancy prediction, the area under curve (AUC) using the endometrial immunology panel was the largest (AUC = 0.766, accuracy: 73.0%). The AUC using the autoantibodies panel was the largest in predicting clinical pregnancy (AUC = 0.688, accuracy: 78.4%), ongoing pregnancy (AUC = 0.802, accuracy: 75.0%), and live birth (AUC = 0.909, accuracy: 89.7%). Combining the data panels did not significantly enhance the effect on prediction of all the four pregnancy outcomes. These results give us a new insight on reproductive immunology and establish the basis for assisting clinicians to plan more precise and personalized diagnosis and treatment for patients with RRF.

The clinical use of time-lapse in human-assisted reproduction

A major challenge in the assisted reproduction laboratory is to set up reproducible and efficient criteria to identify the embryo with the highest developmental potential. Over the years, several methods have been used worldwide with this purpose. Initially, standard morphology assessment was the only available strategy. It is now universally recognized that besides being a very subjective embryo selection strategy, morphology evaluation alone has a very poor prognostic value. More recently, the availability of time-lapse incubators allowed a continuous monitoring of human embryo development. This technology has spread quickly and many fertility clinics over the world produced a remarkable amount of data. To date, however, a general consensus on which variables, or combination of variables, should play a central role in embryo selection is still lacking. Many confounding factors, concerning both patient features and clinical and biological procedures, have been observed to influence embryo development. In addition, several studies have reported unexpected positive outcomes, even in the presence of abnormal developmental criteria. While it does not seem that time-lapse technology is ready to entirely replace the more invasive preimplantation genetic testing in identifying the embryo with the highest implantation potential, it is certainly true that its application is rapidly growing, becoming progressively more accurate. Studies involving artificial intelligence and deep-learning models as well as combining morphokinetic with other non-invasive markers of embryo development, are currently ongoing, raising hopes for its successful applicability for clinical purpose in the near future. The present review mainly focuses on data published starting from the first decade of 2000, when time-lapse technology was introduced as a routine clinical practice in the infertility centers.

Faster fertilization and cleavage kinetics reflect competence to achieve a live birth after intracytoplasmic sperm injection, but this association fades with maternal age

OBJECTIVE

To assess the relationship of early developmental kinetics with competence to provide a live birth and the impact of maternal age in this context.

DESIGN

Retrospective cohort study including 4,915 embryos, of which 1,390 were transferred and provided a clinical outcome paired with morphokinetic data; 168 of them resulted in a live birth (LB), and 1,222 did not (NLB). Early morphokinetic parameters were compared between LB and NLB embryos from patients stratified into two age groups (<37 and ≥37 years), and between embryos at the same competence group from patients aged <37 and ≥37 years. The association of morphokinetic parameters with live birth was tested by univariate and multivariate analyses.

SETTING

Fertility clinic.

PATIENT(S)

The study population included 1,066 patients undergoing autologous intracytoplasmic sperm injection cycles with fresh single (SET), double (DET) or triple (TET) embryo transfers on day 2 or 3. Of them, 669 patients produced NLB embryos and 134 produced LB embryos.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Fertilization and cleavage morphokinetic parameters and live birth.

RESULT(S)

In the total patient population, all morphokinetic parameters were achieved earlier in LB compared with NLB embryos. The same was observed in patients aged <37 years (P<.015), but not ≥37 years. Except for the t8 (time at which an 8-blastomere embryo was identified), all morphokinetic parameters were reached earlier in LB embryos from patients aged <37 years compared with LB embryos from patients aged ≥37 years. Univariate analysis revealed that earlier occurrence of all morphokinetic parameters was associated with live birth, although only earlier t2 (time at which two separate and distinct cells were identified) was associated with live birth independently from maternal age in the multivariate analysis.

CONCLUSION(S)

Despite its retrospective nature and performance in a single IVF center, this study presents novel data indicating that embryos competent to provide a live birth display overall faster early developmental kinetics compared with embryos that do not achieve a live birth after transfer, a difference that, however, narrows as maternal age advances. The findings suggest that fertilization and cleavage morphokinetic parameters may constitute valuable references for embryo selection strategies aiming to improve live birth rates, specifically before advanced maternal age while holding limited usefulness in advanced maternal age.

Time-lapse imaging: the state of the art†

The introduction of time-lapse imaging to clinical in vitro fertilization practice enabled the undisturbed monitoring of embryos throughout the entire culture period. Initially, the main objective was to achieve a better embryo development. However, this technology also provided an insight into the novel concept of morphokinetics, parameters regarding embryo cell dynamics. The vast amount of data obtained defined the optimal ranges in the cell-cycle lengths at different stages of embryo development. This added valuable information to embryo assessment prior to transfer. Kinetic markers became part of embryo evaluation strategies with the potential to increase the chances of clinical success. However, none of them has been established as an international standard. The present work aims at describing new approaches into time-lapse: progress to date, challenges, and possible future directions.

Development of automated annotation software for human embryo morphokinetics

STUDY QUESTION

Is it possible to develop an automated annotation tool for human embryo development in time-lapse devices based on image analysis?

SUMMARY ANSWER

We developed and validated an automated software for the annotation of human embryo morphokinetic parameters, having a good concordance with expert manual annotation on 701 time-lapse videos.

WHAT IS KNOWN ALREADY

Morphokinetic parameters obtained with time-lapse devices are increasingly used for the assessment of human embryo quality. However, their annotation is time-consuming and can be slightly operator-dependent, highlighting the need to develop fully automated approaches.

STUDY DESIGN, SIZE, DURATION

This monocentric study was conducted on 701 videos originating from 584 couples undergoing IVF with embryo culture in a time-lapse device. The only selection criterion was that the duration of the video must be over 60 h.

PARTICIPANTS/MATERIALS, SETTING, METHODS

An automated morphokinetic annotation tool was developed based on gray level coefficient of variation and detection of the thickness of the zona pellucida. The detection of cellular events obtained with the automated tool was compared with those obtained manually by trained experts in clinical settings.

MAIN RESULTS AND THE ROLE OF CHANCE

Although some differences were found when embryos were considered individually, we found an overall concordance between automated and manual annotation of human embryo morphokinetics from fertilization to expanded blastocyst stage (r2 = 0.92).

LIMITATIONS, REASONS FOR CAUTION

These results should undergo multicentric external evaluation in order to test the overall performance of the annotation tool. Getting access to the export of 3D videos would enhance the quality of the correlation with the same algorithm and its extension to the 3D regions of interest. A technical limitation of our work lies within the duration of the video. The more embryo stages the video contains, the more information the script has to identify them correctly.

WIDER IMPLICATIONS OF THE FINDINGS

Our system paves the way for high-throughput analysis of multicentric morphokinetic databases, providing new insights into the clinical value of morphokinetics as a predictor of embryo quality and implantation.

STUDY FUNDING/COMPETING INTEREST(S)

This study was partly funded by Finox-Gedeon Richter Forward Grant 2016 and NeXT (ANR-16-IDEX-0007). We have no conflict of interests to declare.

TRIAL REGISTRATION NUMBER

N/A

The KidscoreTM D5 algorithm as an additional tool to morphological assessment and PGT-A in embryo selection: a time-lapse study

Objective

To evaluate the use of implantation data algorithm KIDscore^TM D5 (Vitrolife^®, Canada) as an additional tool for morphological assessment and preimplantation genetic testing for aneuploidies (PGT-A) to improve implantation and ongoing pregnancy rates.

Materials and Methods

This study looked into 912 embryos from 270 patients who underwent IVF at the INMATER Fertility Clinic in Lima, Peru, between October 2016 and June 2018. All embryos were cultured for up to five or six days in an Embryoscope^® time-lapse incubator (Vitrolife^®, Canada) and evaluated based on the KIDscore^TM D5 algorithm (KS5). Biopsies for PGT-A screening were performed in 778 (85.31%) embryos. A total of 184 single embryo transfers (68% of patients) were performed during the study period and the embryos transferred were divided into four groups: 1) euploid embryos transferred without consideration to their KS5 scores (n=86); 2) euploid embryos transferred considering their KS5 scores (n=48); 3) embryos transferred without consideration to their KS5 scores and that were not evaluated by PGT-A (n=40); and 4) embryos transferred considering their KS5 scores and that were not evaluated by PGT-A (n=10). Implantation and ongoing pregnancy rates were compared between the groups and between euploid embryos with the highest KS5 scores (KS5=6, n=25) and euploid embryos with the lowest KS5 scores (KS5=1, n=51). The correlations between KS5 scores and embryo euploidy rates were also evaluated.

Results

Euploid embryo transfers in which KS5 scores were considered in the selection process had significantly higher Implantation and ongoing pregnancy rates compared to euploid embryo transfers in which selection was based on morphology (75.00% vs. 50.00%; p=0.002 and 66.66% vs. 48.83%; p=0.037, respectively). Additionally, implantation rates were significantly higher for blastocysts with the highest KS5 score (KS5=6) compared to blastocysts with the lowest score (KS5=1) (80.00% vs. 49.02%; p=0.045). Ongoing pregnancy rates were not significantly different (72.00% vs. 47.06%; p=0.105). Euploidy rates were significantly higher in the group of embryos with KS5=6 than in the group of embryos with KS5=1 (61.88% vs. 48.33%; p=0.006).

Conclusion

Embryo selection based on the KS5 algorithm score improved the implantation rates of single euploid blastocyst transfers. Furthermore, embryos with the highest KS5 score had a higher probability of being euploid and implanting.

Artificial intelligence in reproductive medicine

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

Performance of Day 5 KIDScore™ morphokinetic prediction models of implantation and live birth after single blastocyst transfer

PURPOSE

While several studies reported the association between morphokinetic parameters and implantation, few predictive models were developed to predict implantation after day 5 embryo transfer, generally without external validation. The objective of this study was to evaluate the respective performance of 2 commercially available morphokinetic-based models (KIDScore™ Day 5 versions 1 and 2) for the prediction of implantation and live birth after day 5 single blastocyst transfer.

METHODS

This monocentric retrospective study was conducted on 210 ICSI cycles with single day 5 embryo transfer performed with a time-lapse imaging (TLI) system between 2013 and 2016. The association between both KIDScore™ and the observed implantation and live birth rates was calculated, as well as the agreement between embryologist's choice for transfer and embryo ranking by the models.

RESULTS

Implantation and live birth rate were both 35.7%. A significant positive correlation was found between both models and implantation rate (r = 0.96 and r = 0.90, p = 0.01) respectively. Both models had statistically significant but limited predictive power for implantation (AUC 0.60). There was a fair agreement between the embryologists' choice and both models (78% and 61% respectively), with minor differences in case of discrepancies.

CONCLUSIONS

KIDScore™ Day 5 predictive models are significantly associated with implantation rates after day 5 single blastocyst transfer. However, their predictive performance remains perfectible. The use of these predictive models holds promises as decision-making tools to help the embryologist select the best embryo, ultimately facilitating the implementation of SET policy. However, embryologists' expertise remains absolutely necessary to make the final decision.

Correlations between embryo morphokinetic development and maternal age: Results from an intracytoplasmic sperm injection program

Objective

It is widely accepted that aging decreases women’s fertility capacity. The aim of this study was to assess correlations between maternal age and the morphokinetic parameters and cleavage pattern of embryos.

Methods

The morphokinetics of embryos derived from women <30, 30–35, 36–40, and >40 years of age were compared retrospectively in terms of time of second polar body extrusion, time of pronuclei appearance, time of pronuclei fading, and time of two to eight discrete cells (t2–t8). Furthermore, abnormal cleavage patterns such as uneven blastomeres at the two-cell stage, cell fusion (Fu), and trichotomous mitoses (TM) were assessed.

Results

Only t5 occurred later in women aged 36–40 and >40 years when compared with those aged <30 and 30–35 years (p<0.001). Other morphokinetic timing parameters, as well the presence of uneven blastomeres, were comparable between the groups (p>0.05). However, Fu and TM were more common in women aged >40 years than in younger women (p<0.001).

Conclusion

Maternal age was correlated with the cleavage pattern of embryos. Therefore, evaluating embryo morphokinetics may contribute to optimal embryo selection, thereby increasing fertility in patients with advanced maternal age.

Embryo morphokinetics is potentially associated with clinical outcomes of single-embryo transfers in preimplantation genetic testing for aneuploidy cycles

RESEARCH QUESTION

Are the morphokinetics of euploid blastocysts evaluated by a generally applicable algorithm associated with the clinical outcomes of single-embryo transfer (SET)?

DESIGN

Time-lapse microscopy was used to compare morphokinetic variables between expanded blastocysts derived from preimplantation genetic testing for aneuploidy cycles using high-resolution next-generation sequencing (hr-NGS). The clinical efficacy of the morphokinetic algorithm KIDScore D5 was evaluated after euploid SET.

RESULTS

Compared with euploid blastocysts, low-level mosaic blastocysts presented comparable morphokinetic and morphological features. However, high-level mosaic blastocysts exhibited significant delays in t5 (median 51.9 h post insemination (hpi), P = 0.034) (where t is the time for the embryo to reach the specific stage in hours after ICSI or conventional IVF) and t8 (median 58.6 hpi, P = 0.032) accompanied by a prolonged time period for the third cell cycle (median 14.7 h, P = 0.012). A significantly higher incidence (P = 0.011) of multinucleation indicated a susceptibility of high-level mosaic blastocysts to mitotic errors. Only a delay in the time for the embryo to reach the full blastocyst stage (median 106.0 hpi, P = 0.039) was revealed in aneuploid blastocysts, reflecting the reduced formation of good-quality blastocysts (42.6% versus 65.7%, P < 0.001). Euploid blastocysts with specific morphokinetic characteristics were graded using the KIDScore D5 algorithm. Grade C embryos achieved significantly lower rates of clinical pregnancy, implantation and ongoing pregnancy (25%, 25% and 10%, respectively) compared with the grade A (76.2%, 79.4% and 68.3%, respectively) or grade B (62.5%, 66.7% and 62.5%, respectively) embryos (P = 0.0171 to <0.0001).

CONCLUSIONS

Although morphokinetic features appear dissimilar in embryos with different diploid-aneuploid mosaic levels, predicting chromosomal abnormalities using morphokinetics alone is still insufficient. When combined with hr-NGS, use of the generally applicable KIDScore D5 algorithm has the potential to discriminate euploid blastocysts with different developmental competence.

Time-lapse videography for embryo selection/de-selection: a bright future or fading star?

The recent clinical introduction of time-lapse videography into in vitro fertilization laboratories has offered a novel opportunity for embryologists to explore improved methods for embryo selection. While the concept of uninterrupted culture of embryos provided by such systems is welcomed, the current evidence does not support its full application in routine clinical practice. The issue of whether or not algorithms for embryo selection can be extrapolated between laboratories, which may represent a major hurdle to its wide application, is currently gaining increasing attention amongst embryologists worldwide. In this commentary issues identified in time-lapse embryo selection/de-selection algorithms, such as quantitative versus qualitative parameters, are discussed alongside the reference start point for the timing system, and types of datasets used for developing and validating time-lapse algorithms. Considering these factors, alternative future research directions which could potentially solve current issues are proposed.

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence

Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rates remains the focus of the in vitro fertilization scientific community and the strongest driver behind innovative approaches. This scoping review aims to describe and analyze complex models based on mathematics for embryo selection, devices, and software most widely employed in the IVF laboratory and algorithms in the service of the cutting-edge technology of artificial intelligence. Despite their promising nature, the practicing embryologist is the one ultimately responsible for the success of the IVF laboratory and thus the one to approve embracing pioneering technologies in routine practice. Applied mathematics and computational biology have already provided significant insight into the selection of the most competent preimplantation embryo. This review describes the leap of evolution from basic mathematics to bioinformatics and investigates the possibility that computational applications may be the means to foretell a promising future for the IVF clinical practice.