Blastocyst classification systems used in Latin America: is a consensus possible?

Embryo selection through artificial intelligence versus embryologists: a systematic review

STUDY QUESTION

What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists?

SUMMARY ANSWER

AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment.

WHAT IS KNOWN ALREADY

The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection.

STUDY DESIGN SIZE DURATION

The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: ('Artificial intelligence' OR 'Machine Learning' OR 'Deep learning' OR 'Neural network') AND ('IVF' OR 'in vitro fertili*' OR 'assisted reproductive techn*' OR 'embryo'), where the character '*' refers the search engine to include any auto completion of the search term.

PARTICIPANTS/MATERIALS SETTING METHODS

A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist.

MAIN RESULTS AND THE ROLE OF CHANCE

Twenty articles were included in this review. There was no specific embryo assessment day across the studies-Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist's visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59-94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists' assessment following local respective guidelines. Using blind test datasets, the embryologists' accuracy prediction was 65.4% (range 47-75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68-90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58-76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67-98%), while clinical embryologists had a median accuracy of 51% (range 43-59%).

LIMITATIONS REASONS FOR CAUTION

The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality.

WIDER IMPLICATIONS OF THE FINDINGS

AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers' perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation.

STUDY FUNDING/COMPETING INTERESTS

This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare.

REGISTRATION NUMBER

CRD42021256333.

Morphology of inner cell mass: a better predictive biomarker of blastocyst viability

Background

Transfer of embryos at the blastocyst stage is one of the best approaches for achieving a higher success rate in In vitro fertilization (IVF) treatment as it demonstrates an improved uterine and embryonic synchrony at implantation. Despite novel biochemical and genetic markers proposed for the prediction of embryo viability in recent years, the conventional morphological grading of blastocysts remains the classical way of selection in routine practice. This study aims to investigate the association between the morphological features of blastocysts and pregnancy outcomes.

Methods

This prospective study included women undergoing single or double frozen blastocyst transfers following their autologous cycles in a period between October 2020 and September 2021. The morphological grades (A-good, B-average, and C-poor) of inner cell mass (ICM) and trophectoderm (TE) of blastocysts with known implantation were compared to assess their predictive potential of pregnancy outcome. It was further explored by measuring the relationship between the two variables using logistic regression and receiver operating characteristic (ROC) analysis.

Results

A total of 1,972 women underwent frozen embryo transfer (FET) cycles with a total of 3,786 blastocysts. Known implantation data (KID) from 2,060 blastocysts of 1,153 patients were subjected to statistical analysis, the rest were excluded. Implantation rates (IR) from transfer of ICM/TE grades AA, AB, BA, BB were observed as 48.5%, 39.4%, 23.4% and 25% respectively. There was a significantly higher IR observed in blastocysts with ICM grade A (p < 0.001) than those with B irrespective of their TE scores. The analysis of the interaction between the two characteristics confirmed the superiority of ICM over TE as a predictor of the outcome. The rank biserial correlation value for ICM was also greater compared to that of TE (0.11 vs 0.05).

Conclusion

This study confirms that the morphology of ICM of the blastocyst is a stronger predictor of implantation and clinical pregnancy than that of TE and can be utilized as a biomarker of viability.

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).

Melatonin Improves Quality of Repeated-Poor and Frozen-Thawed Embryos in Human, a Prospective Clinical Trial

OBJECTIVE

In this study, two experiments were performed to assess the effect and the role of melatonin on human in vitro embryo quality.

METHODS

Experiment I: A total of 42 repeated-poor-quality-embryo patients were enrolled, with a total of 181 oocytes retrieval cycles. After IVF, for the same patient, the MT cycles group (10^-7 M melatonin added to the culture medium; n=48) were compared with the previous non-MT cycles group (n=133), following by in vitro culture to blastocyst stage and embryo transfer. 31 patients were transplanted with 65 embryo transfer, including 24 MT embryo transfer, 41 non-MT embryo transfer. Cycle outcomes were compared between the two groups. Experiment II:A total of 143 supernumerary human cleavage-stage embryos (from non-repeated-poor-quality-embryo patients) vitrified on Day 3 after IVF were warmed and randomized into two groups: melatonin group (10^-7 M melatonin added to the culture medium; n=71) and control group (n=72), and then cultured for 72 h. Rate of blastocyst and high-quality blastocyst, reactive oxygen species (ROS) levels of culture media as well as embryonic GPX1, CAT, Mn-SOD, Cu/Zn-SOD, BCL-2, BAX gene expression levels were analyzed.

RESULTS

Experiment I: Results showed that the rate of Day 3 high-quality embryos (29.6% vs.19.5%) in the MT cycles group was significantly higher than that in the non-MT cycles group (P<0.05). The rate of available blastocysts (17.1% vs.12.7%) and clinical pregnancy rate (25.0% vs.17.1%) were in tendency higher in the group treated with melatonin (P>0.05). Experiment II:Results showed that the blastocyst rates in the melatonin administered group were significantly higher than in control group (42.25% vs.26.38%, P<0.05). There were no significant differences in high-quality blastocyst rates. In addition, quantitative PCR showed that the expression of CAT was significantly upregulated by melatonin treatment (P<0.05), while there were no significant differences in the expression of GPX1, Mn-SOD, Cu/Zn-SOD, BAX and BCL-2 gene as well as the levels of ROS.

CONCLUSION

These data showed that melatonin supplement in the culture medium will improve Day 3 high-quality embryos rate of repeated-poor-quality-embryo patients and improve blastocyst rate of vitrified-warmed cleavage-stage embryos, suggesting that melatonin intervention may provide a potential rescue strategy for IVF failures.

CLINICAL TRIAL REGISTRATION

identifier [ChiCTR2200059773].

Number of Blastomeres in Day-2 Embryos Affect the Rates of Blastocyst Formation and Clinical Pregnancy During In Vitro Fertilization Cycles

In the current in vitro fertilization and embryo transfer protocol, the 8 blastomeres in the day-3 embryo are selected for transfer because these embryos can produce high rates of blastocyst formation and clinical pregnancy. However, the relationship between the blastomere number in day-2 embryos and the rate of blastocyst formation or clinical pregnancy remains unclear. The purpose of this retrospective study is to explore the relationship between the blastomere number in day-2 embryos and the rate of blastocyst formation or clinical pregnancy. From January 2015 to April 2020, we collected 8126 day-3 embryos (8 blastomeres) from 2282 patients. These embryos were classified into 8 groups (1 blastomere, 2 blastomeres, 3 blastomeres, 4 blastomeres, 5 blastomeres, 6 blastomeres, 7 blastomeres, and 8 blastomeres) based on their blastomeres number on day 2 after insemination. Of these groups, the 4 blastomeres group accounted for the largest proportion (74.44%). The 1 blastomere group accounted for the smallest proportion (0.22%). A total of 3554 day-3 embryos (8 blastomeres) from 1648 patients developed into blastocysts. The rate of blastocyst formation from the 4 blastomeres group was the highest (94.06%). Finally, 800 patients received single day-3 embryos (8 blastomeres) transfer. The rate of clinical pregnancy from 4 blastomeres group was the highest (51.98%). In conclusion, our data provide evidence that the number of blastomeres in day-2 embryos affects the rate of blastocyst formation and clinical pregnancy.

Mining of variables from embryo morphokinetics, blastocyst's morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service

Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra- and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.