Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study

The density of the inner cell mass is a new indicator of the quality of a human blastocyst: a valid supplement to the Gardner scoring system

STUDY QUESTION

Can the density of the inner cell mass (ICM) be a new indicator of the quality of the human blastocyst?

SUMMARY ANSWER

The densification index (DI) developed in this study can quantify ICM density and provide positive guidance for ploidy, pregnancy, and live birth.

WHAT IS KNOWN ALREADY

In evaluating the quality of ICM, reproductive care clinics still use size indicators without further evaluation. The main disadvantage of this current method is that the evaluation of blastocyst ICM is relatively rough and cannot meet the needs of clinical embryologists, especially when multiple blastocysts have the same ICM score, which makes them difficult to evaluate further.

STUDY DESIGN, SIZE, DURATION

This observational study included data from 2272 blastocysts in 1991 frozen-thawed embryo transfer (FET) cycles between January 2018 to November 2021 and 1105 blastocysts in 430 preimplantation genetic testing cycles between January 2019 and February 2023.

PARTICIPANTS/MATERIALS, SETTING, METHODS

FET, ICSI, blastocyst culture, trophectoderm biopsy, time-lapse (TL) monitoring, and next-generation sequencing were performed. After preliminary sample size selection, the 11 focal plane images captured by the TL system were normalized and the spatial frequency was used to construct the DI of the ICM.

MAIN RESULTS AND THE ROLE OF CHANCE

This study successfully constructed a quantitative indicator DI that can reflect the degree of ICM density in terms of fusion and texture features. The higher the DI value, the better the density of the blastocyst ICM, and the higher the chances that the blastocyst was euploid (P < 0.001) and that pregnancy (P < 0.001) and live birth (P = 0.005) were reached. In blastocysts with ICM graded B and blastocysts graded 4BB, DI was also positively associated with ploidy, pregnancy, and live birth (P < 0.05). ROC analysis showed that combining the Gardner scoring system with DI can more effectively predict pregnancy and live births, when compared to using the Gardner scoring system alone.

LIMITATIONS, REASONS FOR CAUTION

Accurate calculation of the DI value places high demands on image quality, requiring manual selection of the clearest focal plane and exposure control. Images with the ICM not completely within the field of view cannot be used. The association between the density of ICM and chromosomal mosaicism was not evaluated. The associations between the density of ICM and different assisted reproductive technologies and different culture conditions in embryo laboratories were also not evaluated. Prospective studies are needed to further investigate the impact of ICM density on clinical outcomes.

WIDER IMPLICATIONS OF THE FINDINGS

ICM density assessment is a new direction in blastocyst assessment. This study explores new ways of assessing blastocyst ICM density and develops quantitative indicators and a corresponding qualitative evaluation scheme for ICM density. The DI of the blastocyst ICM developed in this study is easy to calculate and requires only TL equipment and image processing, providing positive guidance for clinical outcomes. The qualitative evaluation scheme of ICM density can assist embryologists without TL equipment to manually evaluate ICM density. ICM density is a simple indicator that can be used in practice and is a good complement to the blastocyst scoring systems currently used in most centers.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the National Key Research & Development Program of China (2021YFC2700603). The authors report no financial or commercial conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos

BACKGROUND

In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable.

METHODS

We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.

RESULTS

These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709-0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes.

CONCLUSIONS

Our study underscores the AI model's ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial

To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference -1.7%; 95% confidence interval -7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161 .

The potential of self- supervised learning in embryo selection for IVF success

How to select the "best" embryo for transfer is a long-standing question in clinical in vitro fertilization (IVF). Wang et al. proposed a multi-modal self-supervised learning framework for human embryo selection with a high accuracy and generalization ability.

A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning

In vitro fertilization (IVF) has revolutionized infertility treatment, benefiting millions of couples worldwide. However, current clinical practices for embryo selection rely heavily on visual inspection of morphology, which is highly variable and experience dependent. Here, we propose a comprehensive artificial intelligence (AI) system that can interpret embryo-developmental knowledge encoded in vast unlabeled multi-modal datasets and provide personalized embryo selection. This AI platform consists of a transformer-based network backbone named IVFormer and a self-supervised learning framework, VTCLR (visual-temporal contrastive learning of representations), for training multi-modal embryo representations pre-trained on large and unlabeled data. When evaluated on clinical scenarios covering the entire IVF cycle, our pre-trained AI model demonstrates accurate and reliable performance on euploidy ranking and live-birth occurrence prediction. For AI vs. physician for euploidy ranking, our model achieved superior performance across all score categories. The results demonstrate the potential of the AI system as a non-invasive, efficient, and cost-effective tool to improve embryo selection and IVF outcomes.

Label-free, high-throughput holographic imaging to evaluate mammalian gametes and embryos†

Assisted reproduction is one of the significant tools to treat human infertility. Morphological assessment is the primary method to determine sperm and embryo viability during in vitro fertilization cycles. It has the advantage of being a quick, convenient, and inexpensive means of assessment. However, visual observation is of limited predictive value for early embryo morphology. It has led many to search for other imaging tools to assess the reproductive potential of a given embryo. The limitations of visual assessment apply to both humans and animals. One recent innovation in assisted reproduction technology imaging is interferometric phase microscopy, also known as holographic microscopy. Interferometric phase microscopy/quantitative phase imaging is the next likely progression of analytical microscopes for the assisted reproduction laboratory. The interferometric phase microscopy system analyzes waves produced by the light as it passes through the specimen observed. The microscope collects the light waves produced and uses the algorithm to create a hologram of the specimen. Recently, interferometric phase microscopy has been combined with quantitative phase imaging, which joins phase contrast microscopy with holographic microscopy. These microscopes collect light waves produced and use the algorithm to create a hologram of the specimen. Unlike other systems, interferometric phase microscopy can provide a quantitative digital image, and it can make 2D and 3D images of the samples. This review summarizes some newer and more promising quantitative phase imaging microscopy systems for evaluating gametes and embryos. Studies clearly show that quantitative phase imaging is superior to bright field microscopy-based evaluation methods when evaluating sperm and oocytes prior to IVF and embryos prior to transfer. However, further assessment of these systems for efficacy, reproducibility, cost-effectiveness, and embryo/gamete safety must take place before they are widely adopted.

Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images

STUDY QUESTION

Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts?

SUMMARY ANSWER

Generative AI models exhibit the capability to generate high-fidelity human blastocyst images, thereby providing substantial training datasets crucial for the development of robust AI models.

WHAT IS KNOWN ALREADY

The integration of AI into IVF procedures holds the potential to enhance objectivity and automate embryo selection for transfer. However, the effectiveness of AI is limited by data scarcity and ethical concerns related to patient data privacy. Generative adversarial networks (GAN) have emerged as a promising approach to alleviate data limitations by generating synthetic data that closely approximate real images.

STUDY DESIGN, SIZE, DURATION

Blastocyst images were included as training data from a public dataset of time-lapse microscopy (TLM) videos (n = 136). A style-based GAN was fine-tuned as the generative model.

PARTICIPANTS/MATERIALS, SETTING, METHODS

We curated a total of 972 blastocyst images as training data, where frames were captured within the time window of 110-120 h post-insemination at 1-h intervals from TLM videos. We configured the style-based GAN model with data augmentation (AUG) and pretrained weights (Pretrained-T: with translation equivariance; Pretrained-R: with translation and rotation equivariance) to compare their optimization on image synthesis. We then applied quantitative metrics including Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) to assess the quality and fidelity of the generated images. Subsequently, we evaluated qualitative performance by measuring the intelligence behavior of the model through the visual Turing test. To this end, 60 individuals with diverse backgrounds and expertise in clinical embryology and IVF evaluated the quality of synthetic embryo images.

MAIN RESULTS AND THE ROLE OF CHANCE

During the training process, we observed consistent improvement of image quality that was measured by FID and KID scores. Pretrained and AUG + Pretrained initiated with remarkably lower FID and KID values compared to both Baseline and AUG + Baseline models. Following 5000 training iterations, the AUG + Pretrained-R model showed the highest performance of the evaluated five configurations with FID and KID scores of 15.2 and 0.004, respectively. Subsequently, we carried out the visual Turing test, such that IVF embryologists, IVF laboratory technicians, and non-experts evaluated the synthetic blastocyst-stage embryo images and obtained similar performance in specificity with marginal differences in accuracy and sensitivity.

LIMITATIONS, REASONS FOR CAUTION

In this study, we primarily focused the training data on blastocyst images as IVF embryos are primarily assessed in blastocyst stage. However, generation of an array of images in different preimplantation stages offers further insights into the development of preimplantation embryos and IVF success. In addition, we resized training images to a resolution of 256 × 256 pixels to moderate the computational costs of training the style-based GAN models. Further research is needed to involve a more extensive and diverse dataset from the formation of the zygote to the blastocyst stage, e.g. video generation, and the use of improved image resolution to facilitate the development of comprehensive AI algorithms and to produce higher-quality images.

WIDER IMPLICATIONS OF THE FINDINGS

Generative AI models hold promising potential in generating high-fidelity human blastocyst images, which allows the development of robust AI models as it can provide sufficient training datasets while safeguarding patient data privacy. Additionally, this may help to produce sufficient embryo imaging training data with different (rare) abnormal features, such as embryonic arrest, tripolar cell division to avoid class imbalances and reach to even datasets. Thus, generative models may offer a compelling opportunity to transform embryo selection procedures and substantially enhance IVF outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by a Horizon 2020 innovation grant (ERIN, grant no. EU952516) and a Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission to A.S. and M.Z.E., the Estonian Research Council (grant no. PRG1076) to A.S., and the EVA (Erfelijkheid Voortplanting & Aanleg) specialty program (grant no. KP111513) of Maastricht University Medical Centre (MUMC+) to M.Z.E.

TRIAL REGISTRATION NUMBER

Not applicable.

Evaluation of the Clinical Efficacy and Trust in AI-Assisted Embryo Ranking: Survey-Based Prospective Study

BACKGROUND

Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance.

OBJECTIVE

The primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience.

METHODS

This simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists.

RESULTS

With AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P<.001 in the t test. With AI guidance, the average score (accuracy) of the junior group increased to 46.581 (52%), reaching a level similar to that of the senior embryologists of 44.833 (50%), with P=.34. Junior embryologists had a higher level of trust in the AI score.

CONCLUSIONS

This study demonstrates the potential benefits of AI in selecting embryos with high chances of pregnancy, particularly for embryologists with 5 years or less of experience, possibly due to their trust in AI. Thus, using AI as an auxiliary tool in clinical practice has the potential to improve embryo assessment and increase the probability of a successful pregnancy.

An artificial intelligence algorithm to select most viable embryos considering current process in IVF labs

Background

The most common Assisted Reproductive Technology is In-Vitro Fertilization (IVF). During IVF, embryologists commonly perform a morphological assessment to evaluate embryo quality and choose the best embryo for transferring to the uterus. However, embryo selection through morphological assessment is subjective, so various embryologists obtain different conclusions. Furthermore, humans can consider only a limited number of visual parameters resulting in a poor IVF success rate. Artificial intelligence (AI) for embryo selection is objective and can include many parameters, leading to better IVF outcomes.

Objectives

This study sought to use AI to (1) predict pregnancy results based on embryo images, (2) assess using more than one image of the embryo in the prediction of pregnancy but based on the current process in IVF labs, and (3) compare results of AI-Based methods and embryologist experts in predicting pregnancy.

Methods

A data set including 252 Time-lapse Videos of embryos related to IVF performed between 2017 and 2020 was collected. Frames related to 19 ± 1, 43 ± 1, and 67 ± 1 h post-insemination were extracted. Well-Known CNN architectures with transfer learning have been applied to these images. The results have been compared with an algorithm that only uses the final image of embryos. Furthermore, the results have been compared with five experienced embryologists.

Results

To predict the pregnancy outcome, we applied five well-known CNN architectures (AlexNet, ResNet18, ResNet34, Inception V3, and DenseNet121). DeepEmbryo, using three images, predicts pregnancy better than the algorithm that only uses one final image. It also can predict pregnancy better than all embryologists. Different well-known architectures can successfully predict pregnancy chances with up to 75.0% accuracy using Transfer Learning.

Conclusion

We have developed DeepEmbryo, an AI-based tool that uses three static images to predict pregnancy. Additionally, DeepEmbryo uses images that can be obtained in the current IVF process in almost all IVF labs. AI-based tools have great potential for predicting pregnancy and can be used as a proper tool in the future.

Diagnostic or prognostic? Decoding the role of embryo selection on in vitro fertilization treatment outcomes

In this review, we take a fresh look at embryo assessment and selection methods from the perspective of diagnosis and prognosis. On the basis of a systematic search in the literature, we examined the evidence on the prognostic value of different embryo assessment methods, including morphological assessment, blastocyst culture, time-lapse imaging, artificial intelligence, and preimplantation genetic testing for aneuploidy.

Culture media affect sex after IVF treatment-a detailed analysis of explanatory variables

PURPOSE

The purpose of this study was to provide a detailed analysis of clinical and laboratory factors associated with skewed secondary sex ratio (SSR) after ART.

METHOD

Retrospective cohort study of embryos resulting in live births, from frozen and fresh single blastocyst transfers. Embryos were cultured in either G-TL (n = 686) or Sage media (n = 685). Data was analyzed using a multivariate logistic regression model and a mixed model analysis.

RESULTS

Significantly more male singletons were born after culture in Sage media compared to G-TL media (odds ratio (OR) 1.34, 95% CI (1.05, 1.70), P = 0.02). Inner cell mass grade B vs A (OR 1.36 95% CI (1.05, 1.76), P = 0.02) and one previous embryo transfer (OR 1.49, 95% CI (1.03, 2.16), P = 0.03) were associated with a significantly higher probability of male child at birth. Factors associated with a reduced probability of male child were expansion grade 3 vs 5 (OR 0.66, 95% CI (10.45, 0.96), P = 0.03) and trophectoderm grade B vs A (OR 0.57, 95% CI (0.44, 0.74), P = 0.00). Male embryos developed significantly faster in Sage media compared to G-TL media for the stages of blastocyst (- 1.12 h, 95% CI (- 2.12, - 0.12)), expanded blastocyst (- 1.35 h, 95% CI (- 2.34, - 0.35)), and hatched blastocyst (- 1.75 h, 95% CI (- 2.99, - 0.52)).

CONCLUSION

More male children were born after culture in Sage media compared to G-TL media. Male embryo development was affected by culture media. Our observations suggest that culture media impact male embryo quality selectively, thus potentially favoring the selection of male embryos.

How should the best human embryo in vitro be? Current and future challenges for embryo selection

In-vitro fertilization (IVF) aims at overcoming the causes of infertility and lead to a healthy live birth. To maximize IVF efficiency, it is critical to identify and transfer the most competent embryo within a cohort produced by a couple during a cycle. Conventional static embryo morphological assessment involves sequential observations under a light microscope at specific timepoints. The introduction of time-lapse technology enhanced morphological evaluation via the continuous monitoring of embryo preimplantation in vitro development, thereby unveiling features otherwise undetectable via multiple static assessments. Although an association exists, blastocyst morphology poorly predicts chromosomal competence. In fact, the only reliable approach currently available to diagnose the embryonic karyotype is trophectoderm biopsy and comprehensive chromosome testing to assess non-mosaic aneuploidies, namely preimplantation genetic testing for aneuploidies (PGT-A). Lately, the focus is shifting towards the fine-tuning of non-invasive technologies, such as "omic" analyses of waste products of IVF (e.g., spent culture media) and/or artificial intelligence-powered morphologic/morphodynamic evaluations. This review summarizes the main tools currently available to assess (or predict) embryo developmental, chromosomal, and reproductive competence, their strengths, the limitations, and the most probable future challenges.

A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development

BACKGROUND

Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5.

METHODS

We retrospectively analysed the morphokinetics of 575 embryos obtained from 80 women who underwent IVF at our Unit. Embryo morphokinetics was registered using the Geri plus® time-lapse system. Overall, 30 clinical, morphological and morphokinetic variables related to women and embryos were recorded and combined. Some embryos reached the expanded blastocyst stage on day 5 (BL Group, n = 210), some others did not (nBL Group, n = 365).

RESULTS

The novel EmbryoMLSelection framework was developed following four-steps: Feature Selection, Rules Extraction, Rules Selection and Rules Evaluation. Six rules composed by a combination of 8 variables were finally selected, and provided a predictive power described by an AUC of 0.84 and an accuracy of 81%.

CONCLUSIONS

We provided herein a new feature-signature able to identify with an high performance embryos with the best developmental competence to reach the expanded blastocyst stage on day 5. Clear and clinically relevant cut-offs were identified for each considered variable, providing an objective tool for early embryo developmental assessment.

Microfluidic chip as a promising evaluation method in assisted reproduction: A systematic review

The aim of assisted reproductive technology (ART) is to select the high-quality sperm, oocytes, and embryos, and finally achieve a successful pregnancy. However, functional evaluation is hindered by intra- and inter-operator variability. Microfluidic chips emerge as the one of the most powerful tools to analyze biological samples for reduced size, precise control, and flexible extension. Herein, a systematic search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, and IEEE Xplore databases until March 2023. We displayed and prospected all detection strategies based on microfluidics in the ART field. After full-text screening, 71 studies were identified as eligible for inclusion. The percentages of human and mouse studies equaled with 31.5%. The prominent country in terms of publication number was the USA (n = 13). Polydimethylsiloxane (n = 49) and soft lithography (n = 28) were the most commonly used material and fabrication method, respectively. All articles were classified into three types: sperm (n = 38), oocytes (n = 20), and embryos (n = 13). The assessment contents included motility, counting, mechanics, permeability, impedance, secretion, oxygen consumption, and metabolism. Collectively, the microfluidic chip technology facilitates more efficient, accurate, and objective evaluation in ART. It can even be combined with artificial intelligence to assist the daily activities of embryologists. More well-designed clinical studies and affordable integrated microfluidic chips are needed to validate the safety, efficacy, and reproducibility. Trial registration: The protocol was registered in the Open Science Frame REGISTRIES (identification: osf.io/6rv4a).

Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images

This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.

Transcriptomic signature of luteinized cumulus cells of oocytes developing to live birth after women received intracytoplasmic sperm injection

OBJECTIVE

To compare the transcriptome of human cumulus cells (CCs) from oocytes with different outcomes (pregnancy yes/no, live birth [LB] yes/no), to identify noninvasive biomarkers for oocyte selection as well as new therapeutic targets to increase LB rates from assisted reproductive technologies (ART).

DESIGN

Retrospective observational study.

SETTINGS

This study was conducted at a University Hospital in Switzerland.

PATIENTS

Subfertile couples undergoing controlled ovarian superstimulation and intracytoplasmic sperm injection with subsequent unbiopsied embryo transfer below the female age of 43 years.

INTERVENTION(S)

RNA sequencing of CCs from oocytes results in a pregnancy, no pregnancy, LB, or no LB.

MAIN OUTCOME MEASURES

Differential gene expression (DEG) between CCs of oocytes results in "no pregnancy" vs. "pregnancy" and "pregnancy only" vs. "live birth."

RESULTS

Although RNA sequencing did not reveal DEGs when comparing the transcriptomic profiles of the groups "no pregnancy" with "pregnancy," we identified 139 DEGs by comparing "pregnancy only" with "live birth," of which 28 belonged to clusters relevant to successful ART outcomes (i.e., CTGF, SERPINE2, PCK1, HHIP, HS3ST, and BIRC5). A functional enrichment analysis revealed that the transcriptome of CCs associated with LB depicts pathways of extracellular matrix, inflammatory cascades leading to ovulation, cell patterning, proliferation, and differentiation, and silencing pathways leading to apoptosis.

CONCLUSION

We identified a CCs transcriptomic profile associated with LB after embryo transfer that, after further validation, could serve to predict successful ART outcomes. The definition of relevant pathways of CCs related to oocyte competency contributes to a broader understanding of the cumulus oocyte complex and helps identify further therapeutic targets for improving ART success.

A brief history of artificial intelligence embryo selection: from black-box to glass-box

With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.

Embryo ranking agreement between embryologists and artificial intelligence algorithms

OBJECTIVE

To evaluate the degree of agreement of embryo ranking between embryologists and eight artificial intelligence (AI) algorithms.

DESIGN

Retrospective study.

PATIENT(S)

A total of 100 cycles with at least eight embryos were selected from the Weill Cornell Medicine database. For each embryo, the full-length time-lapse (TL) videos, as well as a single embryo image at 120 hours, were given to five embryologists and eight AI algorithms for ranking.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Kendall rank correlation coefficient (Kendall's τ).

RESULT(S)

Embryologists had a high degree of agreement in the overall ranking of 100 cycles with an average Kendall's tau (K-τ) of 0.70, slightly lower than the interembryologist agreement when using a single image or video (average K-τ = 0.78). Overall agreement between embryologists and the AI algorithms was significantly lower (average K-τ = 0.53) and similar to the observed low inter-AI algorithm agreement (average K-τ = 0.47). Notably, two of the eight algorithms had a very low agreement with other ranking methodologies (average K-τ = 0.05) and between each other (K-τ = 0.01). The average agreement in selecting the best-quality embryo (1/8 in 100 cycles with an expected agreement by random chance of 12.5%; confidence interval [CI]95: 6%-19%) was 59.5% among embryologists and 40.3% for six AI algorithms. The incidence of the agreement for the two algorithms with the low overall agreement was 11.7%. Agreement on selecting the same top two embryos/cycle (expected agreement by random chance corresponds to 25.0%; CI95: 17%-32%) was 73.5% among embryologists and 56.0% among AI methods excluding two discordant algorithms, which had an average agreement of 24.4%, the expected range of agreement by random chance. Intraembryologist ranking agreement (single image vs. video) was 71.7% and 77.8% for single and top two embryos, respectively. Analysis of average raw scores indicated that cycles with low diversity of embryo quality generally resulted in a lower overall agreement between the methods (embryologists and AI models).

CONCLUSION(S)

To our knowledge, this is the first study that evaluates the level of agreement in ranking embryo quality between different AI algorithms and embryologists. The different concordance methods were consistent and indicated that the highest agreement was intraembryologist agreement, followed by interembryologist agreement. In contrast, the agreement between some of the AI algorithms and embryologists was similar to the inter-AI algorithm agreement, which also showed a wide range of pairwise concordance. Specifically, two AI models showed intra- and interagreement at the level expected from random selection.

Blastocyst quality and reproductive and perinatal outcomes: a multinational multicentre observational study

STUDY QUESTION

Does the transfer of single low-grade blastocysts result in acceptable reproductive and perinatal outcomes compared to the transfer of single good-grade blastocysts?

SUMMARY ANSWER

The transfer of single low-grade blastocysts resulted in a reduced live birth rate of around 30% (14% for very low-grade blastocysts) compared to 44% for single good-grade blastocysts, but does not lead to more adverse perinatal outcomes.

WHAT IS KNOWN ALREADY

It is known that low-grade blastocysts can result in live births. However, the current studies are limited by relatively small sample sizes and single-centre designs. Furthermore, evidence on perinatal outcomes after transferring low-grade blastocysts is limited.

STUDY DESIGN, SIZE, DURATION

We conducted a multi-centre, multi-national retrospective cohort study of 10 018 women undergoing 10 964 single blastocyst transfer cycles between 2009 and 2020 from 14 clinics across Australia, China, and New Zealand.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Blastocysts were graded individually based on assessment of the morphology and development of the inner cell mass (ICM) and trophectoderm (TE), and were grouped into three quality categories: good- (AB, AB, or BA), moderate- (BB), and low-grade (grade C for ICM or TE) blastocysts. CC blastocysts were individually grouped as very low-grade blastocysts. Logistic regression with generalized estimating equation was used to analyse the association between blastocyst quality and live birth as well as other reproductive outcomes. Binomial, multinomial logistic, or linear regression was used to investigate the association between blastocyst quality and perinatal outcomes. Odds ratio (OR), adjusted OR (aOR), adjusted regression coefficient, and their 95% CIs are presented. Statistical significance was set at P < 0.05.

MAIN RESULTS AND THE ROLE OF CHANCE

There were 4386 good-grade blastocysts, 3735 moderate-grade blastocysts, and 2843 low-grade blastocysts were included in the analysis, for which the live birth rates were 44.4%, 38.6%, and 30.2%, respectively. Compared to good-grade blastocysts, the live birth rate of low-grade blastocysts was significantly lower (aOR of 0.48 (0.41-0.55)). Very low-grade blastocysts were associated with an even lower live birth rate (aOR 0.30 (0.18-0.52)) and their absolute live birth rate was 13.7%. There were 4132 singleton live births included in the analysis of perinatal outcomes. Compared with good-grade blastocysts, low-grade blastocysts had comparable preterm birth rates (<37 weeks, aOR 1.00 (0.65-1.54)), birthweight Z-scores (adjusted regression coefficient 0.02 (0.09-0.14)), and rates of very low birth weight (<1500 g, aOR 0.84 (0.22-3.25)), low birth weight (1500-2500 g, aOR 0.96 (0.56-1.65)), high birth weight (>4500 g, aOR 0.93 (0.37-2.32)), small for gestational age (aOR 1.63 (0.91-2.93)), and large for gestational age (aOR 1.28 (0.97-1.70)).

LIMITATIONS, REASONS FOR CAUTION

Due to the nature of the retrospective design, residual confounding could not be excluded. In addition, the number of events for some perinatal outcomes was small. Between-operator and between-laboratory variations in blastocyst assessment were difficult to control.

WIDER IMPLICATIONS OF THE FINDINGS

Patients undergoing IVF should be informed that low-grade blastocysts result in a lower live birth rate, however they do not increase the risk of adverse perinatal outcomes. Further research should focus on the criteria for embryos that should not be transferred and on the follow-up of long-term outcomes of offspring.

STUDY FUNDING/COMPETING INTEREST(S)

H.Z. is supported by a Monash Research Scholarship. B.W.J.M. is supported by a NHMRC Investigator grant (GNT1176437). R.W. is supported by an NHMRC Emerging Leadership Investigator grant (2009767). B.W.J.M. reports consultancy, travel support, and research funding from Merck. The other authors do not have competing interests to disclose.

TRIAL REGISTRATION NUMBER

N/A.

Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study

RESEARCH QUESTION

What is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial?

DESIGN

This single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions.

RESULTS

The implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001).

CONCLUSIONS

These prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.

Diminished Ovarian Reserve in Endometriosis: Insights from In Vitro, In Vivo, and Human Studies-A Systematic Review

Endometriosis, a prevalent disorder in women of reproductive age, is often associated with undesired infertility. Ovarian reserve, an essential measure of ovarian function that is crucial for maintaining fecundity, is frequently diminished in women with endometriosis. Though the causative relationship between endometriosis and reduced ovarian reserve is not fully understood due to the lack of standardized and precise measurements of ovarian reserve, there is ongoing discussion regarding the impact of interventions for endometriosis on ovarian reserve. Therefore, in this review, we investigate articles that have related keywords and which were also published in recent years. Thereafter, we provide a comprehensive summary of evidence from in vitro, in vivo, and human studies, thereby shedding light on the decreased ovarian reserve in endometriosis. This research consolidates evidence from in vitro, in vivo, and human studies on the diminished ovarian reserve associated with endometriosis, as well as enhances our understanding of whether and how endometriosis, as well as its interventions, contribute to reductions in ovarian reserve. Furthermore, we explore potential strategies to modify existing therapy options that could help prevent diminished ovarian reserve in patients with endometriosis.

Pre-Vitrification and Post-Warming Variables of Vitrified-Warmed Blastocysts That Are Predictable for Implantation

Human IVF embryos that are not used for fresh transfer are cryopreserved by vitrification for later embryo transfers. This study evaluates pre-vitrification and post-warming embryo characteristics that are suitable to predict the chance of clinical pregnancy in single vitrified blastocyst transfer (SVBT) cycles. In a multicenter observational trial (IMBOS trial), embryos were cultured in a time-lapse system before and after vitrification. Associations between clinical pregnancy, morphokinetic parameters, blastocyst collapse, KIDScore D5, pre-vitrification and post-warming Gardner scores, post-warming blastocyst size and re-expansion rates before SVBT were analyzed in 182 SVBTs which resulted in 89 clinical pregnancies. No association was found between clinical pregnancy after SVBT and the number of collapses or the maximal collapse size before vitrification. The multifactorial analysis of pre-vitrification Gardner scores showed a significant association with clinical pregnancy for trophectoderm grading but not for expansion/hatching status and inner cell mass grading. A significant association with clinical pregnancy was found for the time to reach a blastocyst after pronuclear fading (tB-tPNf), KIDScore D5 and post-warming size but not the rate of expansion or maximal expansion size. The selection of blastocysts for SVBT could benefit from using pre-vitrification parameters like tB-tPNf, trophectoderm grading and post-warming blastocyst size.

Spontaneous collapse as a prognostic marker for human blastocysts: a systematic review and meta-analysis

STUDY QUESTION

Is spontaneous collapse (SC) by human blastocysts a prognostic factor in IVF treatment?

SUMMARY ANSWER

SC in human blastocyst is associated with reduced euploid embryo and pregnancy rates.

WHAT IS KNOWN ALREADY

SC of the human blastocyst is a phenomenon that was revealed relatively recently following the clinical application of time-lapse monitoring in IVF laboratories. The ploidy and clinical prognosis of affected blastocysts are still poorly understood, with inconsistent reports. Systematic reviews and meta-analyses on this topic are currently absent in the literature but its potential as a marker of embryo viability holds great clinical value. In this study, we aimed to comprehensively evaluate the potential of SC as a prognostic factor in regard to ploidy status, and pregnancy, live birth and miscarriage rates.

STUDY DESIGN, SIZE, DURATION

A systematic review and meta-analysis were performed according to PRISMA guidelines, with a protocol registered with PROSPERO (CRD42022373749). A search of MEDLINE, EMBASE, and the Cochrane Library for relevant studies was carried out on 10 October 2022, using key words relevant to 'blastocyst collapse' and 'time-lapse imaging'.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Two independent reviewers systematically screened and evaluated each study in terms of participants, exposure, comparator, and outcomes (PECO). The Quality In Prognosis Studies tool was used for quality assessment. Data were extracted according to Cochrane methods. Pregnancy, live birth, ploidy, or miscarriage data were summarized by risk ratios (RRs) or odds ratios and their 95% CIs. All meta-analyses were performed with random-effects models.

MAIN RESULTS AND THE ROLE OF CHANCE

Following removal of duplicates, a total of 196 records were identified by the initial search. After screening according to PECO, 19 articles were included for further eligibility assessment. For meta-analysis, seven retrospective cohort studies were eventually included. After data pooling, the incidence of blastocyst SC was 37.0% (2516/6801) among seven studies (ranging from 17.4% to 56.2%). SC was associated with significantly lower clinical pregnancy rates (two studies, n = 736; RR = 0.77, 95% CI = 0.62-0.95; I2 = 30%), ongoing pregnancy rates (five studies, n = 2503; RR = 0.66, 95% CI = 0.53-0.83; I2 = 60%), and reduced euploidy rates (three studies, n = 3569; RR = 0.70, 95% CI = 0.59-0.83; I2 = 69%). Nevertheless, live birth rates (two studies, n = 816; RR = 0.76, 95% CI = 0.55-1.04; I2 = 56%) and miscarriage rate (four studies, n = 1358; RR = 1.31, 95% CI = 0.95-1.80; I2 = 0%) did not differ between blastocysts with or without SC. There was, however, significant heterogeneity between the studies included for evaluation of ongoing pregnancy rates (I2 = 60%, P = 0.04), live birth rates (I2 = 56%, P = 0.13), and ploidy rates (I2 = 69%, P = 0.04). Subgroup analyses were conducted according to different definitions of SC, number of collapse events, and whether the transferred blastocyst had undergone preimplantation genetic testing for aneuploidy; with inconclusive findings across subgroups.

LIMITATIONS, REASONS FOR CAUTION

All studies in the meta-analysis were retrospective with varying levels of heterogeneity for different outcomes. Not all studies had accounted for potential confounding factors, therefore only unadjusted data could be used in the main meta-analysis. Studies employed slightly different strategies when defining blastocyst SC. Standardization in the definition for SC is needed to improve comparability between future studies.

WIDER IMPLICATIONS OF THE FINDINGS

Our results indicate that blastocyst SC has negative implications for a pregnancy. Such blastocysts should be given a low ranking when selecting from a cohort for intrauterine transfer. Blastocyst SC should be considered as a contributing variable when building blastocyst algorithms to predict pregnancy or live birth.

STUDY FUNDING/COMPETING INTEREST(S)

There is no external funding to report. All authors report no conflict of interest.

REGISTRATION NUMBER

PROSPERO 2022 CRD42022373749.

An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential

Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In this study, 608 blastocysts were selected for transfer using morphokinetics and Gardner criteria. Retrospectively, morphometric parameters of blastocyst size, inner cell mass (ICM) size, ICM-to-blastocyst size ratio, and ICM shape were automatically measured by a semantic segmentation neural network model. The model was trained on 1506 videos with 102 videos for validation with no overlap between the ICM and trophectoderm models. Univariable logistic analysis found blastocyst size and ICM-to-blastocyst size ratio to be significantly associated with implantation potential. Multivariable regression analysis, adjusted for woman age, found blastocyst size to be significantly associated with implantation potential. The odds of implantation increased by 1.74 for embryos with a blastocyst size greater than the mean (147 ± 19.1 μm). The performance of the algorithm was represented by an area under the curve of 0.70 (p < 0.01). In conclusion, this study supports the association of a large blastocyst size with higher implantation potential and suggests that automatically measured blastocyst morphometrics can be used as a precise, consistent, and time-saving tool for improving blastocyst selection.

Making and selecting the best embryo in the laboratory

Over the past 4 decades our ability to maintain a viable human embryo in vitro has improved dramatically, leading to higher implantation rates. This has led to a notable shift to single blastocyst transfer and the ensuing elimination of high order multiple gestations. Future improvements to embryo culture systems will not only come from new improved innovative media formulations (such as the inclusion of antioxidants), but plausibly by moving away from static culture to more dynamic perfusion-based systems now made a reality owing to the breakthroughs in three-dimensional printing technology and micro fabrication. Such an approach has already made it feasible to create high resolution devices for intracytoplasmic sperm injection, culture, and cryopreservation, paving the way not only for improvements in outcomes but also automation of assisted reproductive technology. Although improvements in culture systems can lead to further increases in pregnancy outcomes, the ability to quantitate biomarkers of embryo health and viability will reduce time to pregnancy and decrease pregnancy loss. Currently artificial intelligence is being used to assess embryo development through image analysis, but we predict its power will be realized through the creation of selection algorithms based on the integration of information related to metabolic functions, cell-free DNA, and morphokinetics, thereby using vast amounts of different data types obtained for each embryo to predict outcomes. All of this will not only make assisted reproductive technology more effective, but it will also make it more cost effective, thereby increasing patient access to infertility treatment worldwide.

Predicting the likelihood of live birth: an objective and user-friendly blastocyst grading system

RESEARCH QUESTION

Can day-5 blastocysts be ranked according to their likelihood of live birth using an objective and user-friendly grading system?

DESIGN

A retrospective multicentre study conducted between 2017 and 2019, including 1044 day-5 blastocysts. Blastocyst expansion degree, trophectoderm and inner cell mass quality were assessed morphologically and morphometrically. Several analyses were conducted: the association between the qualitative and quantitative assessment for the blastocyst expansion degree and the number of trophectoderm cells; the effect of the embryo quality on day 3 and the contribution of the three blastocyst parameters to live birth, with logistic regression; and a decision tree with the most significant variables to create the new scoring system.

RESULTS

Cut-off points were found to discriminate between expanding and expanded blastocysts (165 µm for blastocyst diameter) and between trophectoderm grades (A: ≥14 cells; B: 11-13 cells; C: ≤10 cells). When the embryos reached the blastocyst stage, their quality on day 3 did not add predictive value for implantation and live birth. In the logistic regression analysis, the only parameter capable of significantly predicting the live birth likelihood was the trophectoderm grade: A versus C (OR 1.95, 95% CI 1.26 to 3.0); B versus C (OR 1.71, 95% CI 1.22 to 2.4). The decision tree supported the finding that the trophectoderm grade had the highest predictive value for a live birth, followed by the blastocyst expansion degree in a second step.

CONCLUSIONS

This new method makes objective blastocyst assessment feasible, allowing for standardization and exportation to other laboratories worldwide.

Cleavage Patterns of 9600 Embryos: The Importance of Irregular Cleavage

This prospective, observational study investigated the incidence of irregular cleavage (IRC) among human embryos and its influence on IVF treatment outcomes. It included 1001 women who underwent 1976 assisted reproduction treatments during 2016-2021 in a single IVF clinic. Embryo morphokinetics were analyzed and evaluated for the association between IRC and women's characteristics, treatment characteristics, and pregnancy outcomes. The incidence of IRC was 17.5% (1689/9632 embryos). Of these, 85% of the embryos had one IRC, 15% had multiple IRC and 35% of IRC events occurred during the embryo's first cell cycle. IRC embryos were found to correlate with male factor (p = 0.01) and higher ICSI rate (p = 0.01). Age, BMI, parity, basal FSH level, stimulation protocol, and number of retrieved oocytes did not differ between groups. Embryos with early IRC or more than one IRC had lower blastulation rates (p = 0.01 for each). Fresh cycles with IRC embryos had a lower clinical pregnancy rate (p = 0.01) and embryos with early IRC had a lower live birth rate (p = 0.04) compared to embryos without IRC. Frozen transfer cycles of blastocyst embryos, with or without IRC, had comparable results. In conclusion, the number of abnormal cleavage events and their timing are important factors in the prognosis of the developing human embryo.

[Interest of iDAScore (intelligent Data Analysis Score) for embryo selection in routine IVF laboratory practice: Results of a preliminary study]

INTRODUCTION

Embryo selection is a major challenge in ART, especially since the generalization of single embryo transfer, and its optimization could lead to the improvement of clinical results in IVF. Recently, several Artificial Intelligence (AI) models, based on deep-learning such as iDAScore, have been developed. These models, trained on time-lapse videos of embryos with known implantation data, can predict the probability of pregnancy for a given embryo, allowing automatization and standardization in embryo selection.

MATERIAL AND METHODS

In this study, we have compared the hierarchical categorization of 311 D5 blastocysts of iDAScore v1.0 and the embryologists of our unit. These 311 D5 blastocysts have been classified as top (70.1%), good (Q+: 10.6%) and poor (Q-: 19.3%) quality by embryologists according to Gardner classification. Median iDAScores were [9.9-8.4],]8.4-7.5] and]7.5-2.1] for top, good and poor-quality blastocysts respectively.

RESULTS

We observed a significantly concordant categorization between iDAScore and embryologists for top, good and poor-quality blastocysts (respectively, 89.5, 36.4 and 48.3%, P < 10-4). Moreover, the hierarchical categorization of the three best blastocysts between iDAScore and the embryologists was as follow: 1st rank: 71.9%; 2nd rank: 61.6%; 3rd rank: 56.8% (P=0.07). One hundred and fifty-one blastocysts with known implantation data were analyzed. The iDAScore of blastocysts that implanted was significantly higher than those that did not implant (implantation+: 9.10±0.57; implantation-: 8.70±0.95, P=0.003).

CONCLUSION

This preliminary study shows that iDAScore is able to perform a reproducible, reliable and immediate hierarchical classification of blastocysts. Moreover, this tool can identify the blastocysts with the highest implantation potential. If these results confirmed on a larger scale of embryos and patients, IA could revolutionize IVF laboratories by standardizing embryo hierarchical selection.

Impact of polyunsaturated fatty acid supplementation on assisted reproductive technology outcomes: a systematic review

This review explores the impact of polyunsaturated fatty acid (PUFA) supplementation in women undergoing assisted reproductive technology (ART) on reproductive outcomes. A systematic search of English peer-reviewed journals was carried out using MEDLINE, EMBASE, and the Cochrane Library to identify articles published from January 1978 to 2021. The primary outcomes assessed included pregnancy and live birth rates. Secondary outcome measures included: (i) implantation rate; (ii) fertilisation rate; (iii) number of oocytes retrieved; (iv) number of metaphase II (MII) oocytes; (v) blastocyst conversion; and (vi) embryo quality. A total of 4 randomised control trials (RCTs) met the inclusion criteria. There is a lack of high-quality research to support widespread dietary supplementation with PUFAs in women undergoing ART. Prior to its clinical recommendation, there is a need for well-designed RCTs to facilitate an in-depth understanding of PUFA supplementation in women undergoing ART.

Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation

PURPOSE

Our objective was to design an automated deep learning model that extracts the morphokinetic events of embryos that were recorded by time-lapse incubators. Using automated annotation, we set out to characterize the temporal heterogeneity of preimplantation development across a large number of embryos.

METHODS

To perform a retrospective study, we used a dataset of video files of 67,707 embryos from four IVF clinics. A convolutional neural network (CNN) model was trained to assess the developmental states that appear in single frames from 20,253 manually-annotated embryos. Probability-weighted superposition of multiple predicted states was permitted, thus accounting for visual uncertainties. Superimposed embryo states were collapsed onto discrete series of morphokinetic events via monotonic regression of whole-embryo profiles. Unsupervised K-means clustering was applied to define subpopulations of embryos of distinctive morphokinetic profiles.

RESULTS

We perform automated assessment of single-frame embryo states with 97% accuracy and demonstrate whole-embryo morphokinetic annotation with R-square 0.994. High quality embryos that had been valid candidates for transfer were clustered into nine subpopulations, as characterized by distinctive developmental dynamics. Retrospective comparative analysis of transfer versus implantation rates reveals differences between embryo clusters as marked by poor synchronization of the third mitotic cell-cleavage cycle.

CONCLUSIONS

By demonstrating fully automated, accurate, and standardized morphokinetic annotation of time-lapse embryo recordings from IVF clinics, we provide practical means to overcome current limitations that hinder the implementation of morphokinetic decision-support tools within clinical IVF settings due to inter-observer and intra-observer manual annotation variations and workload constrains. Furthermore, our work provides a platform to address embryo heterogeneity using dimensionality-reduced morphokinetic descriptions of preimplantation development.

Non-Coding RNAs as Biomarkers for Embryo Quality and Pregnancy Outcomes: A Systematic Review and Meta-Analysis

Despite advances in in vitro fertilization (IVF), there is still a lack of non-invasive and reliable biomarkers for selecting embryos with the highest developmental and implantation potential. Recently, small non-coding RNAs (sncRNAs) have been identified in biological fluids, and extracellular sncRNAs are explored as diagnostic biomarkers in the prediction of IVF outcomes. To determine the predictive role of sncRNAs in embryo quality and IVF outcomes, a systematic review and meta-analysis was performed. Articles were retrieved from PubMed, EMBASE, and Web of Science from 1990 to 31 July 2022. Eighteen studies that met the selection criteria were analyzed. In total, 22 and 47 different sncRNAs were found to be dysregulated in follicular fluid (FF) and embryo spent culture medium (SCM), respectively. MiR-663b, miR-454 and miR-320a in FF and miR-20a in SCM showed consistent dysregulation in two different studies. The meta-analysis indicated the potential predictive performance of sncRNAs as non-invasive biomarkers, with a pooled area under curve (AUC) value of 0.81 (95% CI 0.78, 0.844), a sensitivity of 0.79 (95% CI 0.72, 0.85), a specificity of 0.67 (95% CI 0.52, 0.79) and a diagnostic odds ratio (DOR) of 8 (95% CI 5, 12). Significant heterogeneity was identified among studies in sensitivity (I2  =  46.11%) and specificity (I2  =  89.73%). This study demonstrates that sncRNAs may distinguish embryos with higher developmental and implantation potentials. They can be promising non-invasive biomarkers for embryo selection in ART. However, the significant heterogeneity among studies highlights the demand for prospective multicenter studies with optimized methods and adequate sample sizes in the future.

Morphometric assessment of blastocysts: relationship with the ongoing pregnancy rate

Objective

To explore a morphometric grading system for blastocysts that is associated with ongoing pregnancy.

Design

Cross-sectional study.

Setting

None.

Patientss

All consecutive vitrified blastocysts at our center from July 2018 to November 2021 that were transferred in single blastocyst transfer cycles until January 2022.

Interventions

None.

Main Outcome Measures

The ongoing pregnancy rate after a single vitrified-warmed blastocyst transfer. Interobserver agreement on morphometric values among embryologists.

Results

Three morphometric variables (blastocyst diameter, area of inner cell mass [ICM], and the estimated trophectoderm cell count) were used to evaluate the expansion, ICM, and trophectoderm morphology. During the study period, 585 blastocysts were involved in this study. Of the 3 morphometric variables, ICM area (per 500 μm², adjusted odds ratio, 1.19; 95% confidence interval, 1.09-1.30) and estimated trophectoderm cell count (per 10 cells, adjusted odds ratio, 1.25; 95% confidence interval, 1.12-1.39) were significantly associated with the ongoing pregnancy rate after adjustment for confounding factors. The ongoing pregnancy rate was 2.0% (1/49) with an ICM area of <2,500 μm² and the estimated trophectoderm cell count <70. The ongoing pregnancy rate reached 47.8% (22/46) when the ICM area and the estimated trophectoderm cell count were >3,500 μm² and >110, respectively. Interobserver agreement on the blastocyst diameter, ICM area, and the estimated trophectoderm cell count was excellent-to-good among 5 embryologists (intraclass correlation coefficients: 0.99, 0.87, and 0.91, respectively).

Conclusions

Morphometric values of ICM and trophectoderm are promising predictors of pregnancy success. The high reproducibility suggests that the morphometric variables will contribute to identifying blastocysts with the highest developmental potential as well as those that will not result in a successful pregnancy.

Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles

Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value.

Training and competency assessment of Clinical Embryologists and licensing of the profession in European countries

STUDY QUESTION

How is the acquisition and testing of theoretical and practical knowledge in Clinical Embryology and the licensing of ART laboratory personnel carried out in European countries?

SUMMARY ANSWER

Twelve out of 31 European countries have established some kind of verification of laboratory competency and skills in ART: in 7 countries, this was related to licensing, but where organized education for Clinical Embryologists existed, there were vast differences in the way these processes were undertaken.

WHAT IS KNOWN ALREADY

In 2015, a report by the ESHRE Embryology Certification Committee concluded that regardless of the large number of people working in IVF laboratories, Clinical Embryology was only recognized as an official profession in 3 out of 27 European national health systems. In most countries, Clinical Embryologists needed to be officially registered under an alternative profession and there were limited opportunities for organized education in this specialist field. Five years after this report, the ESHRE Working Group on Embryologist Training Analysis conducted a survey to collect detailed information about how Clinical Embryologists from different European countries are acquiring their theoretical knowledge and practical skills in ART, and how their level of education and competence in Clinical Embryology is verified.

STUDY DESIGN SIZE DURATION

Two questionnaires about the possibilities for acquiring the education and training needed to work in ART and verification of this knowledge were prepared by the ESHRE Working Group on Embryologist Training Analysis. The first was sent in 2020 to a panel of invited lead European Embryologists who attended an Expert Meeting held in Rome, Italy. In order to have a more comprehensive and updated picture, in 2021 the same survey was also sent to the ESHRE Committee of National Representatives (CNRs). At the end of 2021, the second survey with specific questions, more focused on Clinical Embryologists' training and licencing, was sent to the CNRs who reported on verification of education in Clinical Embryology.

PARTICIPANTS/MATERIALS SETTING METHODS

The first survey consisted of 17 questions. It was initially submitted to 14 lead Embryologists and then resubmitted to the 34 ESHRE CNRs. Representatives from 31 countries responded. A second survey with 23 questions was sent to the 12 ESHRE CNRs who reported an established national system of verification of education in Clinical Embryology, with specific questions focused on the training of Clinical Embryologists. All 12 CNRs responded.

MAIN RESULTS AND THE ROLE OF CHANCE

Analysis showed that European national education programmes in Clinical Embryology could be split into 4 categories: non-existent (13 countries), recommended (5 countries), simple compulsory (9 countries), and complex compulsory (4 countries). A national document stating the minimum education requirements for staff to work in an IVF laboratory was reported by 19 respondents. The requirement to follow a prescribed theoretical and laboratory training programme in ART was compulsory in 9 and 10 countries, respectively. Some form of verification of laboratory skills, theoretical knowledge in ART, and continuing professional development was required in 12, 10, and 9 countries, respectively. A national trainee's logbook format was reported by seven respondents and a national tutorial system was available in six countries. Only seven countries had official licensing of ART laboratory staff. The title of Clinical Embryologist was not recognized in 13 countries and in 6 countries, it was used only by professional bodies, while in 12 countries the profession was at least cited in governmental regulations. The ESHRE Clinical Embryologist Certificate was officially recognized in eight countries.

LIMITATIONS REASONS FOR CAUTION

The survey took place in two steps and the results were then combined to provide a representative picture for most of the European countries sampled. The vast majority, but not all, of the CNRs answered the request to participate in the survey.

WIDER IMPLICATIONS OF THE FINDINGS

The professional recognition of Clinical Embryology within Europe is steadily evolving. However, it remains a concern that many countries continue to not recognize Clinical Embryology as a profession, with a vast difference in the reported organization of educational and training programmes and verification of skills. It is recommended that a training programme for Clinical Embryology and ART in Europe should be standardized and relevant issues should be addressed by competent authorities and European Union institutions. ESHRE is best placed to take a leading role in this educational process.

STUDY FUNDING/COMPETING INTERESTS

The Working Group members who are the authors of this article did not receive payments for the completion of this study. The authors have no conflicts of interest to declare.

Molecular regulators of human blastocyst development and hatching: Their significance in implantation and pregnancy outcome

In humans, blastocyst hatching and implantation events are two sequential, critically linked and rate-limiting events for a prospective pregnancy. These events are regulated by embryo-endometrium derived molecular factors which include hormones, growth factors, cytokines, immune-modulators, cell adhesion molecules and proteases. Due to poor viability of blastocysts, they fail to hatch and implant, leading to a low 'Live Birth Rates', majorly contributing to infertility. Here, embryo-derived biomarkers analysis plays a key role to assess potential biological viability of blastocysts which are capable of implantation and prospective pregnancy. Thus far, embryo-derived biomarkers examined are mostly immune-modulators which are thought to be associated with blastocyst development-implantation and progression of pregnancy, leading to live births. There is an urgent need to develop a quantitative and a reliable non-invasive approach aiding embryo selection for elective single embryo transfer and to minimize recurrent pregnancy loss and multiple pregnancies. In this article, we provide a comprehensive review on our current knowledge and understanding of potential embryo-derived molecular regulators, that is, biomarkers, of development of human blastocysts, their hatching and implantation. We discuss their potential implications in the assessment of blastocyst implantation potential and pregnancy outcome in terms of live births in humans.

Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI)

PURPOSE

Deep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in the embryo culture environment which may impact clinical outcomes. The present study aimed to evaluate the utility of an AI-QA tool to consistently monitor ART staff performance (MD and embryologist) in embryo transfer (ET), embryo vitrification (EV), embryo warming (EW), and trophectoderm biopsy (TBx).

METHODS

Pregnancy outcomes from groups of 20 consecutive elective single day 5 blastocyst transfers were evaluated for the following procedures: MD performed ET (N = 160 transfers), embryologist performed ET (N = 160 transfers), embryologist performed EV (N = 160 vitrification procedures), embryologist performed EW (N = 160 warming procedures), and embryologist performed TBx (N = 120 biopsies). AI-generated implantation probabilities for the same embryo cohorts were estimated, as were mean AI-predicted and actual implantation rates for each provider and compared using Wilcoxon singed-rank test.

RESULTS

Actual implantation rates following ET performed by one MD provider: "H" was significantly lower than AI-predicted (20% vs. 61%, p = 0.001). Similar results were observed for one embryologist, "H" (30% vs. 60%, p = 0.011). Embryos thawed by embryologist "H" had lower implantation rates compared to AI prediction (25% vs. 60%, p = 0.004). There were no significant differences between actual and AI-predicted implantation rates for EV, TBx, or for the rest of the clinical staff performing ET or EW.

CONCLUSIONS

AI-based QA tools could provide accurate, reproducible, and efficient staff performance monitoring in an ART practice.

Assuring quality in assisted reproduction laboratories: assessing the performance of ART Compass - a digital art staff management platform

PURPOSE

Staff management is the most cited ART/IVF laboratory inspection deficiency. Small ART/IVF clinics may be challenged to perform these activities by low staff volume; similarly, large ART/IVF networks may be challenged by high staff volume and large datasets. Here, we sought to investigate the performance of an automated, digital platform solution to manage this necessary task.

METHODS

The ART Compass (ARTC) digital staff management platform was used to assess the clinical decision-making of ART laboratory staff. The survey modules presented standardized instructions to technologists and measured inter- and intra-technologist variability for subjective "clinical decision-making" type questions. Internal and external comparisons were achieved by providing technologists two answers: (1) a comparison to their own lab director and (2) to the most popular response collectively provided by all lab director level accounts. The platform is hosted on HIPAA compliant Amazon web servers, accessible via web browser and mobile applications for iOS (Apple) and Android mobile devices.

RESULTS

Here, we investigated the performance of a digital staff management platform for single embryologist IVF practices and for three IVF lab networks (sites A, B, C) from 2020 to 2022. Embryology dish preparation survey results show variance among respondents in the following: PPE use, media volume, timing of oil overlay, and timing of moving prepared dishes to incubators. Surveying the perceived Gardner score and terms in use for early blastocysts reveals a lack of standardization of terminology and fair to poor agreement. We observed moderate inter-technologist agreement for ICM and TE grade (0.47 and 0.52, respectively). Lastly, the clinical decision of choice to freeze or discard an embryo revealed that agreement to freeze was highest for the top-quality embryos, and that some embryos can be highly contested, evenly split between choice to freeze or discard.

CONCLUSIONS

We conclude that a digital platform is a novel and effective tool to automate, routinely monitor, and assure quality for staff-related parameters in ART and IVF laboratories. Use of a digital platform can increase regulatory compliance and provide actionable insight for quality assurance in both single embryologist practices and for large networks. Furthermore, clinical decision-making can be augmented with artificial intelligence integration.

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study

BACKGROUND

One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information.

METHODS

In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC).

FINDINGS

Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability.

INTERPRETATION

As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A.

FUNDING

US National Institutes of Health.

Embryo Morphokinetic Activity Evident in Short Videos of In Vitro Bovine Embryos

Embryo transfer (ET) and in vitro fertilization (IVF) are increasing in use by dairy producers as a means to breed their animals as these assisted reproductive techniques can optimize the genetics of the dairy breed or enable “beef on dairy” programs to increase the profitability of the dairy. Due to the advantages of ET and IVF, it is anticipated that their use will continue to increase despite the status of underwhelmingly low pregnancy outcomes. Pregnancy rates of bovine ET/IVF remain below 56%, with many dairy producers implementing beef on dairy programs reporting pregnancy to be lower than 23%. The inability to objectively evaluate embryo health prior to transfer into a recipient is a contributing factor to this problem as 20% of transferred embryos are inviable at the time of transfer and have little chance of establishing a pregnancy. The objective of this research was to evaluate bovine embryo real-time morphokinetic activity based on 30 s video recordings of day 7.5 morulas and correlate morphokinetic activity to developmental outcomes. Eighty-eight embryos were recorded in standard embryo culture conditions with an SMZ-1000 Stereo zoom microscope and TE-300 Nikon inverted microscope. The difference in the embryo’s morphokinetic activity was measured frame-by-frame and correlated to embryo hatching outcomes. It was found that embryos with lower morphokinetic activity demonstrated higher hatching rates and developmental outcomes, suggesting measurement of embryo morphokinetic activity is a noninvasive and non-subjective method to evaluate embryo competency prior to transfer and can be used to improve the reproductive efficiency and profitability of IVF/ET of dairy cattle.

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.

Human blastocyst spontaneous collapse is associated with worse morphological quality and higher degeneration and aneuploidy rates: a comprehensive analysis standardized through artificial intelligence

STUDY QUESTION

What are the factors associated with human blastocyst spontaneous collapse and the consequences of this event?

SUMMARY ANSWER

Approximately 50% of blastocysts collapsed, especially when non-viable, morphologically poor and/or aneuploid.

WHAT IS KNOWN ALREADY

Time-lapse microscopy (TLM) is a powerful tool to observe preimplantation development dynamics. Lately, artificial intelligence (AI) has been harnessed to automate and standardize such observations. Here, we adopted AI to comprehensively portray blastocyst spontaneous collapse, namely the phenomenon of reduction in size of the embryo accompanied by efflux of blastocoel fluid and the detachment of the trophectoderm (TE) from the zona pellucida (ZP). Although the underlying causes are unknown, blastocyst spontaneous collapse deserves attention as a possible marker of reduced competence.

STUDY DESIGN, SIZE, DURATION

An observational study was carried out, including 2348 TLM videos recorded during preimplantation genetic testing for aneuploidies (PGT-A, n = 720) cycles performed between January 2013 and December 2020. All embryos in the analysis at least reached the time of starting blastulation (tSB), 1943 of them reached full expansion, and were biopsied and then vitrified.

PARTICIPANTS/MATERIALS, SETTING, METHODS

ICSI, blastocyst culture, TE biopsy without Day 3 ZP drilling, comprehensive chromosome testing and vitrification were performed. The AI software automatically registered tSB and time of expanding blastocyst (tEB), start and end time of each collapse, time between consecutive collapses, embryo proper area, percentage of shrinkage, embryo:ZP ratio at embryo collapse, time of biopsy (t-biopsy) and related area of the fully (re-)expanded blastocyst before biopsy, time between the last collapse and biopsy. Blastocyst morphological quality was defined according to both Gardner's criteria and an AI-generated implantation score. Euploidy rate per biopsied blastocyst and live birth rate (LBR) per euploid single embryo transfer (SET) were the main outcomes. All significant associations were confirmed through regression analyses. All couple, cycle and embryo main features were also investigated for possible associations with blastocyst spontaneous collapse.

MAIN RESULTS AND THE ROLE OF CHANCE

At least one collapsing embryo (either viable or subsequently undergoing degeneration) was recorded in 559 cycles (77.6%) and in 498 cycles (69.2%) if considering only viable blastocysts. The prevalence of blastocyst spontaneous collapse after the tSB, but before the achievement of full expansion, was 50% (N = 1168/2348), irrespective of cycle and/or couple characteristics. Blastocyst degeneration was 13% among non-collapsing embryos, while it was 18%, 20%, 26% and 39% among embryos collapsing once, twice, three times or ≥4 times, respectively. The results showed that 47.3% (N = 918/1943) of the viable blastocysts experienced at least one spontaneous collapse (ranging from 1 up to 9). Although starting from similar tSB, the number of spontaneous collapses was associated with a delay in both tEB and time of biopsy. Of note, the worse the quality of a blastocyst, the more and the longer its spontaneous collapses. Blastocyst spontaneous collapse was significantly associated with lower euploidy rates (47% in non-collapsing and 38%, 32%, 31% and 20% in blastocysts collapsing once, twice, three times or ≥4 times, respectively; multivariate odds ratio 0.78, 95%CI 0.62-0.98, adjusted P = 0.03). The difference in the LBR after euploid vitrified-warmed SET was not significant (46% and 39% in non-collapsing and collapsing blastocysts, respectively).

LIMITATIONS, REASONS FOR CAUTION

An association between chromosomal mosaicism and blastocyst collapse cannot be reliably assessed on a single TE biopsy. Gestational and perinatal outcomes were not evaluated. Other culture strategies and media should be tested for their association with blastocyst spontaneous collapse. Future studies with a larger sample size are needed to investigate putative impacts on clinical outcomes after euploid transfers.

WIDER IMPLICATIONS OF THE FINDINGS

These results demonstrate the synergistic power of TLM and AI to increase the throughput of embryo preimplantation development observation. They also highlight the transition from compaction to full blastocyst as a delicate morphogenetic process. Blastocyst spontaneous collapse is common and associates with inherently lower competence, but additional data are required to deepen our knowledge on its causes and consequences.

STUDY FUNDING/COMPETING INTEREST(S)

There is no external funding to report. I.E., A.B.-M., I.H.-V. and B.K. are Fairtility employees. I.E. and B.K. also have stock or stock options of Fairtility.

TRIAL REGISTRATION NUMBER

N/A.

Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study

Propose

Does an annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonatal outcomes?

Methods

Patients who underwent SVBT cycles (3010 cycles, mean age: 39.3 ± 4.0). Scores were calculated using the iDAScore software module in the Vitrolife Technology Hub (Vitrolife, Gothenburg, Sweden). The correlation between iDAScore, LB rates, and total miscarriage (TM), including 1st- and 2nd-trimester miscarriage, was analysed using a trend test and multivariable logistic regression analysis. Furthermore, the correlation between the iDAScore and neonatal outcomes was analysed.

Results

LB rates decreased as iDAScore decreased (P < 0.05), and a similar inverse trend was observed for the TM rates. Additionally, multivariate logistic regression analysis showed that iDAScore significantly correlated with increased LB (adjusted odds ratio: 1.811, 95% CI: 1.666–1.976, P < 0.05) and decreased TM (adjusted odds ratio: 0.799, 95% CI: 0.706–0.905, P < 0.05). There was no significant correlation between iDAScore and neonatal outcomes, including congenital malformations, sex, gestational age, and birth weight. Multivariate logistic regression analysis, which included maternal and paternal age, maternal body mass index, parity, smoking, and presence or absence of caesarean section as confounding factors, revealed no significant difference in any neonatal characteristics.

Conclusion

Automatic embryo scoring using iDAScore correlates with decreased miscarriage and increased LB and has no correlation with neonatal outcomes.

Supplementary information

The online version contains supplementary material available at 10.1007/s10815-022-02562-5.

Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques

BACKGROUND

Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditional method of manual embryo assessment is time-consuming and highly susceptible to inter- and intra-observer variability. Automation of this process results in more objective and accurate predictions.

METHOD

In this paper, we propose a novel methodology based on deep learning to automatically evaluate the morphological appearance of human embryos from time-lapse imaging. A supervised contrastive learning framework is implemented to predict embryo viability at day 4 and day 5, and an inductive transfer approach is applied to classify embryo quality at both times.

RESULTS

Results showed that both methods outperformed conventional approaches and improved state-of-the-art embryology results for an independent test set. The viability result achieved an accuracy of 0.8103 and 0.9330 and the quality results reached values of 0.7500 and 0.8001 for day 4 and day 5, respectively. Furthermore, qualitative results kept consistency with the clinical interpretation.

CONCLUSIONS

The proposed methods are up to date with the artificial intelligence literature and have been proven to be promising. Furthermore, our findings represent a breakthrough in the field of embryology in that they study the possibilities of embryo selection at day 4. Moreover, the grad-CAMs findings are directly in line with embryologists' decisions. Finally, our results demonstrated excellent potential for the inclusion of the models in clinical practice.

An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction

Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).

Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis

Artificial intelligence (AI) has been gaining support in the field of in vitro fertilization (IVF). Despite the promising existing data, AI cannot yet claim gold-standard status, which serves as the rationale for this study. This systematic review and data synthesis aims to evaluate and report on the predictive capabilities of AI-based prediction models regarding IVF outcome. The study has been registered in PROSPERO (CRD42021242097). Following a systematic search of the literature in Pubmed/Medline, Embase, and Cochrane Central Library, 18 studies were identified as eligible for inclusion. Regarding live-birth, the Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) was 0.905, while the partial AUC (pAUC) was 0.755. The Observed: Expected ratio was 1.12 (95%CI: 0.26–2.37; 95%PI: 0.02–6.54). Regarding clinical pregnancy with fetal heartbeat, the AUC of the SROC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54–1.05; 95%PI: 0.21–1.62). According to this data synthesis, the majority of the AI-based prediction models are successful in accurately predicting the IVF outcome regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat, and ploidy status. This review attempted to compare between AI and human prediction capabilities, and although studies do not allow for a meta-analysis, this systematic review indicates that the AI-based prediction models perform rather similarly to the embryologists’ evaluations. While AI models appear marginally more effective, they still have some way to go before they can claim to significantly surpass the clinical embryologists’ predictive competence.

Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences

Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60-0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.