Clinical validation of an automatic classification algorithm applied on cleavage stage embryos: analysis for blastulation, euploidy, implantation, and live-birth potential.

STUDY QUESTION: Is a commercially available embryo assessment algorithm for early embryo evaluation based on the automatic annotation of morphokinetic timings a useful tool for embryo selection in IVF cycles? SUMMARY ANSWER: The classification provided by the algorithm was shown to be significantly predictive, especially when combined with conventional morphological evaluation, for development to blastocyst, implantation, and live birth, but not for euploidy. WHAT IS KNOWN ALREADY: The gold standard for embryo selection is still morphological evaluation conducted by embryologists. Since the introduction of time-lapse technology to embryo culture, many algorithms for embryo selection have been developed based on embryo morphokinetics, providing complementary information to morphological evaluation. However, manual annotations of developmental events and application of algorithms can be time-consuming and subjective processes. The introduction of automation to morphokinetic annotations is a promising approach that can potentially reduce subjectivity in the embryo selection process and improve the workflow in IVF laboratories. STUDY DESIGN, SIZE, DURATION: This observational, retrospective cohort study was performed in a single IVF clinic between 2018 and 2021 and included 3736 embryos from oocyte donation cycles (423 cycles) and 1291 embryos from autologous cycles with preimplantation genetic testing for aneuploidies (PGT-A, 185 cycles). Embryos were classified on Day 3 with a score from 1 (best) to 5 (worst) by the automatic embryo assessment algorithm. The performance of the embryo classification model for blastocyst development, implantation, live birth, and euploidy prediction was assessed. PARTICIPANTS/MATERIALS, SETTING, METHODS: All embryos were monitored by a time-lapse system with an automatic cell-tracking and embryo assessment software during culture. The embryo assessment algorithm was applied on Day 3, resulting in embryo classification from 1 to 5 (from highest to lowest developmental potential) depending on four parameters: P2 (t3-t2), P3 (t4-t3), oocyte age, and number of cells. There were 959 embryos selected for transfer on Day 5 or 6 based on conventional morphological evaluation. The blastocyst development, implantation, live birth, and euploidy rates (for embryos subjected to PGT-A) were compared between the different scores. The correlation of the algorithm scoring with the occurrence of those outcomes was quantified by generalized estimating equations (GEEs). Finally, the performance of the GEE model using the embryo assessment algorithm as the predictor was compared to that using conventional morphological evaluation, as well as to a model using a combination of both classification systems. MAIN RESULTS AND THE ROLE OF CHANCE: The blastocyst rate was higher with lower the scores generated by the embryo assessment algorithm. A GEE model confirmed the positive association between lower embryo score and higher odds of blastulation (odds ratio (OR) (1 vs 5 score) = 15.849; P < 0.001). This association was consistent in both oocyte donation and autologous embryos subjected to PGT-A. The automatic embryo classification results were also statistically associated with implantation and live birth. The OR of Score 1 vs 5 was 2.920 (95% CI 1.440-5.925; P = 0.003; E = 2.81) for implantation and 3.317 (95% CI 1.615-6.814; P = 0.001; E = 3.04) for live birth. However, this association was not found in embryos subjected to PGT-A. The highest performance was achieved when combining the automatic embryo scoring and traditional morphological classification (AUC for implantation potential = 0.629; AUC for live-birth potential = 0.636). Again, no association was found between the embryo classification and euploidy status in embryos subjected to PGT-A (OR (1 vs 5) = 0.755 (95% CI 0.255-0.981); P = 0.489; E = 1.57). LIMITATIONS, REASONS FOR CAUTION: The retrospective nature of this study may be a reason for caution, although the large sample size reinforced the ability of the model for embryo selection. WIDER IMPLICATIONS OF THE FINDINGS: Time-lapse technology with automated embryo assessment can be used together with conventional morphological evaluation to increase the accuracy of embryo selection process and improve the success rates of assisted reproduction cycles. To our knowledge, this is the largest embryo dataset analysed with this embryo assessment algorithm. STUDY FUNDING/COMPETING INTEREST(S): This research was supported by Agencia Valenciana de Innovació and European Social Fund (ACIF/2019/264 and CIBEFP/2021/13). In the last 5 years, M.M. received speaker fees from Vitrolife, Merck, Ferring, Gideon Richter, Angelini, and Theramex, and B.A.-R. received speaker fees from Merck. The remaining authors have no competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

The use of morphokinetics as a predictor of  implantation: a multicentric study to define and validate an algorithm for embryo selection.

STUDY QUESTION: Can we use morphokinetic markers to select the embryos most likely to implant and are the results likely to be consistent across different clinics? SUMMARY ANSWER: Yes, morphokinetic markers can be used to select the embryos most likely to implant and the results were similar in different IVF clinics that share methods and organization to some extent. WHAT IS KNOWN ALREADY: With the introduction of time-lapse technology several authors have proposed the use of kinetic markers to improve embryo selection. The majority of these markers can be detected as early as Day 2 of development. Morphology remains the gold standard but kinetic markers have been proven as excellent tools to complement our decisions. Nevertheless, the majority of time-lapse studies are based on small data sets deriving from one single clinic. STUDY DESIGN, SIZE, DURATION: Retrospective multicentric study of 1664 cycles of which 799 were used to develop an algorithm (Phase 1 of the study) and 865 to test its predictive power (Phase 2 of the study). PARTICIPANTS/MATERIALS, SETTING, METHODS: University-affiliated infertility centres patients undergoing first or second ICSI cycle using their own or donated oocytes. Embryo development was analysed with a time-lapse imaging system. Variables studied included the timing to two cells (t2), three cells (t3), four cells (t4) and five cells (t5) as well as the length of the second cell cycle (cc2 = t3 - t2) and the synchrony in the division from two to four cells (s2 = t4 - t3). Implantation (IR) and clinical pregnancy (CPR) rates were also analysed. MAIN RESULTS AND THE ROLE OF CHANCE: During Phase 1 of the study we identified three variables most closely related to implantation: t3 (34-40 h), followed by cc2 (9-12 h) and t5 (45-55 h). Based on these results we elaborated an algorithm that classified embryos from A to D according to implantation potential. During Phase 2 of the study the algorithm was validated in a different group of patients that included 865 cycles and 1620 embryos transferred. In this phase of the study, embryos were categorized based on the algorithm and significant differences in IR were observed between the different categories ('A' 32%, 'B' 28%, 'C' 26%, 'D' 20% and 'E' 17%, P < 0.001). In addition we identified three quality criteria: direct cleavage from one to three cells, uneven blastomere size in second cell cycle and multinucleation in third cell cycle. LIMITATIONS, REASONS FOR CAUTION: The retrospective nature of the study limits its potential value, although the use of one database to generate the algorithm (embryos from this database were not selected by any morphokinetic criteria) and one database to validate it reinforces our conclusions. WIDER IMPLICATIONS OF THE FINDINGS: The elaboration of an algorithm based on a larger database derived from different (albeit related) clinics raises the possibility that such algorithms could be applied in different clinical settings.