Does embryo categorization by existing artificial intelligence, morphokinetic or morphological embryo selection models correlate with blastocyst euploidy rates?

The Istanbul Consensus update: a revised ESHRE/ALPHA consensus on oocyte and embryo static and dynamic morphological assessment† ‡

This European Society of Human Reproduction and Embryology (ESHRE)/Alpha Scientists in Reproductive Medicine (ALPHA) consensus document provides several novel recommendations to assess oocyte and embryo morphology and rank embryos for transfer. A previous ALPHA/ESHRE consensus on oocyte and embryo morphological assessment was published in 2011. After more than a decade, and the integration of time-lapse technology into embryo culture and assessment, a thorough review and update was needed. A working group consisting of ALPHA members and ESHRE Special interest group of Embryology members formulated recommendations on oocyte and embryo assessment. The working group included 17 internationally recognized experts with extensive experience in clinical embryology. Seven members represented ALPHA and eight members represented ESHRE, along with two methodological experts from the ESHRE central office. Based on a systematic literature search and discussion of existing evidence, the recommendations of the Istanbul Consensus (2011) were reassessed and, where appropriate, updated based on consensus within the working group. A stakeholder review was organized after the updated draft was finalized. The final version was approved by the working group, the ALPHA Executive Committee and the ESHRE Executive Committee. This updated consensus paper provides 20 recommendations focused on the timeline of preimplantation developmental events and morphological criteria for oocyte, zygote and embryo assessment. Based on the duration of embryo culture, recommendations are given on the frequency and timing of assessments to ensure consistency and effectiveness. Several criteria relevant to oocyte and embryo morphology have not been well studied, leading to either a recommendation against their use for grading or for their use in ranking rather than grading. Future updates may require further revision of these recommendations. This document provides embryologists with advice on best practices when assessing oocyte and embryo quality based on the most recent evidence.

The Istanbul consensus update: a revised ESHRE/ALPHA consensus on oocyte and embryo static and dynamic morphological assessment†,‡

STUDY QUESTION

What are the current recommended criteria for morphological assessment of oocytes, zygotes, and embryos?

SUMMARY ANSWER

The present ESHRE/Alpha Scientists in Reproductive Medicine consensus document provides several novel recommendations to assess oocyte and embryo morphology and rank embryos for transfer.

WHAT IS KNOWN ALREADY

A previous Alpha Scientists in Reproductive Medicine/ESHRE consensus on oocyte and embryo morphological assessment was published in 2011. After more than a decade, and the integration of time-lapse technology into embryo culture and assessment, a thorough review and update was needed.

STUDY DESIGN, SIZE, DURATION

A working group consisting of Alpha Scientists in Reproductive Medicine executive committee members and ESHRE Special interest group of Embryology members formulated recommendations on oocyte and embryo assessment.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The working group included 17 internationally recognized experts with extensive experience in clinical embryology. Seven members represented Alpha Scientists in Reproductive Medicine and eight members represented ESHRE, along with to two methodological experts from the ESHRE central office. Based on a systematic literature search and discussion of existing evidence, the recommendations of the Istanbul Consensus (2011) were reassessed and, where appropriate, updated based on consensus within the working group. A stakeholder review was organized after the updated draft was finalized. The final version was approved by the working group, the Alpha executive committee and the ESHRE Executive Committee.

MAIN RESULTS AND THE ROLE OF CHANCE

This updated consensus paper provides 20 recommendations focused on the timeline of preimplantation developmental events and morphological criteria for oocyte, zygote, and embryo assessment. Based on duration of embryo culture, recommendations are given on the frequency and timing of assessments to ensure consistency and effectiveness.

LIMITATIONS, REASONS FOR CAUTION

Several criteria relevant to oocyte and embryo morphology have not been well studied, leading to either a recommendation against their use for grading or for their use in ranking rather than grading. Future updates may require further revision of these recommendations.

WIDER IMPLICATIONS OF THE FINDINGS

This document provides embryologists with advice on best practices when assessing oocyte and embryo quality based on the most recent evidence.

STUDY FUNDING/COMPETING INTEREST(S)

The consensus meeting and writing of the paper were supported by funds from ESHRE and Alpha Scientists in Reproductive Medicine. The working group members did not receive any payment. G.C. declared payments or honoraria for lectures from Gedeon Richter and Cooper Surgical. A.C. declared text book royalties (Mastering Clinical Embryology, published 2024), consulting fees from Cooper Surgical, Gedeon Richter and TMRW Life Sciences, honoraria for lectures from Merck, Ferring, and Gedeon Richter, and participation in the HFEA Scientific Advances Committee; she also disclosed being treasurer and vice-president of Alpha Scientists in Reproductive Medicine, a shareholder in Care Fertility Limited and Fertile Mind Limited, and having stock options in TMRW Life Sciences and U-Ploid Biotechnology Ltd. L.R. declared consulting fees from Organon, payments or honoraria for lectures from Merck, Organon, IBSA, Finox, Geden Richter, Origio, Organon, Ferring, Fundation IVI; she also disclosed being a member of the Advisory Scientific Board of IVIRMA (Paid) and a member of the Advisory Scientific Board of Nterilizer (unpaid). I.S. declared payments or honoraria for lectures from Vitrolife and Cooper Surgical, and stock options from Alife Health. M.A. declared payments or honoraria for lectures from Vitrolife and support for attending meetings from Vitrolife and Cooper Surgical (both unrelated to this manuscript). The other authors have no conflicts of interest to declare.

DISCLAIMER

This Good Practice Recommendations (GPRs) document represents the consensus views of the members of this working group based on the scientific evidence available at the time of the meeting. GPRs should be used for information and educational purposes. They should not be interpreted as setting a standard of care or be deemed inclusive of all proper methods of care or be exclusive of other methods of care reasonably directed to obtaining the same results. They do not replace the need for application of clinical judgement to each individual presentation, or variations based on locality and facility type.

Deep learning applications for human embryo assessment using time-lapse imaging: scoping review

Background

The integration of deep learning (DL) and time-lapse imaging technologies offers new possibilities for improving embryo assessment and selection in clinical in vitro Fertilization (IVF).

Objectives

This scoping review aims to explore the range of deep learning model applications in the evaluation and selection of embryos monitored through time-lapse imaging systems.

Methods

A total of 6 electronic databases (Scopus, MEDLINE, EMBASE, ACM Digital Library, IEEE Xplore, and Google Scholar) were searched for peer-reviewed literature published before May 2024. We adhered to the PRISMA guidelines for reporting scoping reviews.

Results

Out of the 773 articles reviewed, 77 met the inclusion criteria. Over the past four years, the use of DL in embryo analysis has increased rapidly. The primary applications of DL in the reviewed studies included predicting embryo development and quality (61%, n = 47) and forecasting clinical outcomes, such as pregnancy and implantation (35%, n = 27). The number of embryos involved in the studies exhibited significant variation, with a mean of 10,485 (SD = 35,593) and a range from 20 to 249,635 embryos. A variety of data types have been used, namely images of blastocyst-stage embryos (47%, n = 36), followed by combined images of cleavage and blastocyst stages (23%, n = 18). Most of the studies did not provide maternal age details (82%, n = 63). Convolutional neural networks (CNNs) were the predominant deep learning architecture used, accounting for 81% (n = 62) of the studies. All studies utilized time-lapse video images (100%) as training data, while some also incorporated demographics, clinical and reproductive histories, and IVF cycle parameters. Most studies utilized accuracy as the discriminative measure (58%, n = 45).

Conclusion

Our results highlight the diverse applications and potential of deep learning in clinical IVF and suggest directions for future advancements in embryo evaluation and selection techniques.

External validation of a fully automated evaluation tool: a retrospective analysis of 68,471 scored embryos

OBJECTIVE

To externally validate a fully automated embryo classification system for in vitro fertilization (IVF) treatments.

DESIGN

Retrospective cohort study.

SUBJECTS

A total of 6,434 patients undergoing 7,352 IVF treatments contributed 70,456 embryos.

EXPOSURE

Embryos were evaluated by conventional morphology and retrospectively scored using a fully automated deep learning-based algorithm across conventional IVF, oocyte donation, and preimplantation genetic testing for aneuploidy (PGT-A) cycles.

MAIN OUTCOME MEASURES

The primary outcomes were implantation and live birth, including odds ratios (ORs) from generalized estimating equation models. Secondary outcomes were embryo morphology, euploidy, and miscarriage. Exploratory outcomes included a comparison between conventional methodology and artificial intelligence algorithm with areas under the receiver operating characteristics curves (AUCs), agreement degree between artificial intelligence and embryologists, Cohen's Kappa coefficient, and relative risk.

RESULTS

Implantation and live birth rates increased as the automatic embryo scores increased. The generalized estimating equation model, controlling for confounders, showed that the automatic score was associated with an OR of 1.31 (95% confidence interval [CI], 1.25-1.36) for implantation in treatments using oocytes from patients and an OR of 1.17 (95% CI, 1.14-1.20) in the oocyte donation program, with no significant association with PGT-A treatments. For live birth, the ORs were 1.27 (95% CI, 1.21-1.33) for patients, 1.16 (95% CI, 1.13-1.19) for donors, and 1.05 (95% CI, 1-1.10) for PGT-A cycles. The average score was higher in embryos with better morphology, in euploid embryos compared with aneuploid embryos, and in embryos that resulted in a full-term pregnancy compared with those that miscarried. Concordance between the highest-scoring embryo and the embryo with the best conventional morphology was 71.4% (95% CI, 67.7%-75.0%) in treatments with patient oocytes and 61.0% (95% CI, 58.6%-63.4%) in the oocyte donation program. Overall, the Cohen's Kappa coefficient was 0.63. The automatic embryo score showed similar AUCs to conventional morphology, although implantation was higher when the transferred embryo matched the highest-scoring embryo from each cohort (57.36% vs. 49.98%). Relative risk indicated a 1.14-fold increase in implantation likelihood when the top-ranked embryo was transferred.

CONCLUSIONS

A fully automated embryo scoring system effectively ranked embryos based on their potential for implantation and live birth. The performance of the conventional methodology was comparable to that of the artificial intelligence-based technology; however, better clinical outcomes were observed when the highest-scoring embryo in the cohort was transferred.

Pregnancy is influenced by more than just embryo ploidy: a retrospective study on preimplantation genetic testing

BACKGROUND

Assisted reproductive technology (ART) has been widely used to treat infertility for more than four decades, but its efficacy is still lower than expected. Therefore, further exploration of the factors that affect the pregnancy outcome of ART treatment is necessary.

MATERIALS AND METHODS

A retrospective study of chromosome rearrangement carrier couples who requested preimplantation genetic testing (PGT) for structural rearrangements at the Fourth Hospital of Shijiazhuang was conducted between February 2019 and December 2022. Multivariate logistic regression analysis was performed to determine the risk factors for pregnancy.

RESULTS

In total, 113 couples were transferred with a single euploid blastocyst, and 77 couples achieved pregnancy. Women with good-quality embryos transferred had a higher probability of pregnancy than women with poor-quality embryos transferred (OR 6.149, 95% CI 2.026-18.658). The chance of pregnancy was higher in women with a pregnancy history than in women without a pregnancy history (OR 3.181, 95% CI 1.157-8.747). The progesterone level on the day of trigger was positively associated with pregnancy (OR 2.605, 95% CI 1.226-5.538).

CONCLUSION

Embryo quality is significantly associated with the pregnancy rate in patients treated with PGT. Embryo ploidy is just one of the factors affecting embryo development. Future studies should focus on the molecular mechanisms of embryo development and develop corresponding detection methods.

Embryologist staffing in assisted reproductive technology laboratories: An international comparative review

Background

Embryologists are crucial in assisted reproductive technology (ART), yet their duties, education, and licensing requirements vary significantly across countries, complicating the determination of optimal staffing levels in ART laboratories. With anticipated advancements such as automation in ART laboratories, this review comprehensively analyzes factors necessary for appropriate future staffing.

Main Findings

A comprehensive literature search was conducted using PubMed to identify relevant articles up to July 2024, employing keywords such as "embryologist," "staffing," and "certification." Articles were evaluated for content related to laboratory operations, and guidelines from five organizations regarding licensing and education were compared.

Results

The review revealed significant international differences in embryologist certification, duties, and staffing recommendations. These disparities, along with the integration of advanced ART technologies and regulatory requirements, significantly impact future staffing needs in ART laboratories.

Conclusion

The definitions of an ART cycle and required staffing levels vary across organizations, influenced by the certification and duties of embryologists in different countries. Adequate embryologist staffing is essential for ensuring laboratory quality control and impacting patient ART outcomes. As new technologies and automation reshape laboratory workflows, collaborative efforts among organizations, countries, and embryologist associations are essential for developing comprehensive educational curricula and determining appropriate staffing levels.

Advancements and Challenges in Preimplantation Genetic Testing for Aneuploidies: In the Pathway to Non-Invasive Techniques

The evolution of preimplantation genetic testing for aneuploidy (PGT-A) techniques has been crucial in assisted reproductive technologies (ARTs), improving embryo selection and increasing success rates in in vitro fertilization (IVF) treatments. Techniques ranging from fluorescence in situ hybridization (FISH) to next-generation sequencing (NGS) have relied on cellular material extraction through biopsies of blastomeres at the cleavage stage on day three or from trophectoderm (TE) cells of the blastocyst. However, this has raised concerns about its potential impact on embryo development. As a result, there has been growing interest in developing non-invasive techniques for detecting aneuploidies, such as the analysis of blastocoel fluid (BF), spent culture medium (SCM), and artificial intelligence (AI) models. Non-invasive methods represent a promising advancement in PGT-A, offering the ability to detect aneuploidies without compromising embryo viability. This article reviews the evolution and principles of PGT-A, analyzing both traditional techniques and emerging non-invasive approaches, while highlighting the advantages and challenges associated with these methodologies. Furthermore, it explores the transformative potential of these innovations, which could optimize genetic screening and significantly improve clinical outcomes in the field of assisted reproduction.

Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study

BACKGROUND

Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete features from 2D images. Here we aim to predict euploidy in human blastocysts using quantitative morphological parameters obtained by 3D morphology measurement.

METHODS

Multi-view images of 226 blastocysts on Day 6 were captured by manually rotating blastocysts during the preparation stage of trophectoderm biopsy. Quantitative morphological parameters were obtained by 3D morphology measurement. Six machine learning models were trained using 3D morphological parameters as the input and PGT-A results as the ground truth outcome. Model performance, including sensitivity, specificity, precision, accuracy and AUC, was evaluated on an additional test dataset. Model interpretation was conducted on the best-performing model.

RESULTS

All the 3D morphological parameters were significantly different between euploid and non-euploid blastocysts. Multivariate analysis revealed that three of the five parameters including trophectoderm cell number, trophectoderm cell size variance and inner cell mass area maintained statistical significance (P < 0.001, aOR = 1.054, 95% CI 1.034-1.073; P = 0.003, aOR = 0.994, 95% CI 0.991-0.998; P = 0.010, aOR = 1.003, 95% CI 1.001-1.006). The accuracy of euploidy prediction by the six machine learning models ranged from 80 to 95.6%, and the AUCs ranged from 0.881 to 0.984. Particularly, the decision tree model achieved the highest accuracy of 95.6% (95% CI 84.9-99.5%) with the AUC of 0.978 (95% CI 0.882-0.999), and the extreme gradient boosting model achieved the highest AUC of 0.984 (95% CI 0.892-1.000) with the accuracy of 93.3% (95% CI 81.7-98.6%). No significant difference was found between different age groups using either decision tree or extreme gradient boosting to predict euploid blastocysts. The quantitative criteria extracted from the decision tree imply that euploid blastocysts have a higher number of trophectoderm cells, larger inner cell mass area, and smaller trophectoderm cell size variance compared to non-euploid blastocysts.

CONCLUSIONS

Using quantitative morphological parameters obtained by 3D morphology measurement, the decision tree-based machine learning model achieved an accuracy of 95.6% and AUC of 0.978 for predicting euploidy in Day 6 human blastocysts.

TRIAL REGISTRATION

N/A.

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction

BACKGROUND

The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments.

METHODS

In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9.

RESULTS

Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics.

CONCLUSIONS

This study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.

EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report EVATOM: a machine-learning assisted embryo health assessment tool utilizing an optical quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with, to the best of our knowledge, novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

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

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN, SIZE, DURATION

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

PARTICIPANTS/MATERIALS, SETTING, METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS, REASONS FOR CAUTION

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

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTEREST(S)

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

TRIAL REGISTRATION NUMBER

N/A.

Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study

RESEARCH QUESTION

What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed blastocyst transfer (SVBT) in a deep-learning model?

DESIGN

A total of 3960 SVBT cycles were retrospectively analysed. Embryos were stratified according to the Society for Assisted Reproductive Technology age groups. Embryos were scored by deep-learning models iDAScore v1.0 (IDA-V1) and iDAScore v2.0 (IDA-V2) (15% more training data than v1.0) and by Gardner grading. The discriminative performance of the pregnancy prediction for each embryo scoring model was compared using the area under the curve (AUC) of the receiver operating characteristic curve for each maternal age group.

RESULTS

The AUC of iDA-V2, iDA-V1 and Gardener grading in all cohort were 0.736, 0.720 and 0.702, respectively. iDA-V2 was significantly higher than iDA-V1 and Gardener grading (P < 0.0001). Group > 35 years (n = 757): the AUC of iDA-V2 was significantly higher than Gardener grading (0.718 versus 0.694, P = 0.015); group aged 35-37 years (n = 821), the AUC of iDA-V2 was significantly higher than iDA-V1 (0.712 versus 0.696, P = 0.035); group aged 41-42 years (n = 715, the AUC of iDA-V2 was significantly higher than Gardener grading (0.745 versus 0.696, P = 0.007); group > 42 years (n = 660) and group aged 38-40 years (n = 1007), no significant differences were found between the groups.

CONCLUSION

The performance of deep learning models for pregnancy prediction will be improved by increasing the size of the training data.

Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation

RESEARCH QUESTION

Do cell numbers and degree of fragmentation in cleavage-stage embryos, assessed manually, correlate with evaluations made by deep learning algorithm model iDAScore v2.0?

DESIGN

Retrospective observational study (n = 5040 embryos; 1786 treatments) conducted at two Swedish assisted reproductive technology centres between 2016 and 2021. Fresh single embryo transfer was carried out on days 2 or 3 after fertilization. Embryo evaluation using iDAScore v2.0 was compared with manual assessment of numbers of cells and grade of fragmentation, analysed by video sequences.

RESULTS

Data from embryos transferred on days 2 and 3 showed that having three or fewer cells compared with four or fewer cells on day 2, and six or fewer cells versus seven to eight cells on day 3, correlated significantly with a difference in iDAScore (medians 2.4 versus 4.0 and 2.6 versus 4.6 respectively; both P < 0.001). The iDAScore for 0-10% fragmentation was significantly higher compared with the groups with higher fragmentation (P < 0.001). When combining cell numbers and fragmentation, iDAScore values decreased as fragmentation increased, regardless of cell number. iDAScore discriminated between embryos that resulted in live birth or no live birth (AUC of 0.627 and 0.607), compared with the morphological model (AUC of 0.618 and 0.585) for day 2 and day 3, respectively.

CONCLUSIONS

The iDAScore v2.0 values correlated significantly with cell numbers and fragmentation scored manually for cleavage-stage embryos on days 2 and 3. iDAScore had some predictive value for live birth, conditional that embryo selection was based on morphology.

Comparison of 1-year cumulative live birth and perinatal outcomes following single blastocyst transfer with or without preimplantation genetic testing for aneuploidy: a propensity score-matched study

PURPOSE

We evaluated whether preimplantation genetic testing for aneuploidy (PGT-A) could increase the cumulative live birth rate (CLBR) in patients with recurrent implantation failure (RIF) and recurrent pregnancy loss (RPL).

METHODS

The clinical records of 7,668 patients who underwent oocyte retrieval (OR) with or without PGT-A were reviewed for 365 days and retrospectively analyzed. Using propensity score matching, 579 patients in the PGT-A group were matched one-to-one with 7,089 patients in the non-PGT-A (control) group. Their pregnancy and perinatal outcomes and CLBRs were statistically compared.

RESULTS

The live birth rate per single vitrified-warmed blastocyst transfers (SVBTs) significantly improved in the PGT-A group in all age groups (P < 0.0002, all). Obstetric and perinatal outcomes were comparable between both groups regarding both RIF and RPL cases. Cox regression analysis demonstrated that in the RIF cases, the risk ratio per OR was significantly lower in the PGT-A group than in the control group (P = 0.0480), particularly in women aged < 40 years (P = 0.0364). However, the ratio was comparable between the groups in RPL cases. The risk ratio per treatment period was improved in the PGT-A group in both RIF and RPL cases only in women aged 40-42 years (P = 0.0234 and P = 0.0084, respectively).

CONCLUSION

Increased CLBR per treatment period was detected only in women aged 40-42 years in both RIF and RPL cases, suggesting that PGT-A is inappropriate to improve CLBR per treatment period in all RIF and RPL cases.

Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM)

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report a machine-learning assisted embryo health assessment tool utilizing a quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

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.