Inter-laboratory agreement on embryo classification and clinical decision: Conventional morphological assessment vs. time lapse

Assessment of artificial intelligence model and manual morphokinetic annotation system as embryo grading methods for successful live birth prediction: a retrospective monocentric study

PURPOSE

The introduction of the time-lapse monitoring system (TMS) and the development of predictive algorithms could contribute to the optimal embryos selection for transfer. Therefore, the present study aims at investigating the efficiency of KIDScore and iDAScore systems for blastocyst stage embryos in predicting live birth events.

METHODS

The present retrospective study was conducted in a private IVF Unit setting throughout a 10-month period from October 2021 to July 2022, and included the analysis of 429 embryos deriving from 91 IVF/ICSI cycles conducted due to infertility of various etiologies. Embryos incubated at the Embryoscope+ timelapse incubator were analyzed through the established scoring systems: KIDScore and iDAScore®. The main outcome measure was the comparison of the two scoring systems in terms of live birth prediction. Embryos with the higher scores at day 5 (KID5 score/iDA5 score) were transferred or cryopreserved for later use.

RESULTS

Embryos with high KID5 and iDA5 scores positively correlated with the probability of successful live birth, with KID5 score yielding a higher efficiency in predicting a successful reproductive outcome compared to a proportionally high iDA5 score. KID5 demonstrated conservative performance in successfully predicting live birth compared to iDA5 score, indicating that an efficient prediction can be either provided by a relatively lower KID5 score or a relatively higher iDA5 score.

CONCLUSION

The developed artificial intelligence tools should be implemented in clinical practice in conjunction with the conventional morphological assessment for the conduction of optimized embryo transfer in terms of a successful live birth.

Criteria for implementing artificial intelligence systems in reproductive medicine

This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.

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.

Reproductive outcomes with delayed blastocyst development: the clinical value of day 7 euploid blastocysts in frozen embryo transfer cycles

Embryos of optimal development reach blastocyst stage 116 ± 2 h after insemination. Usable D7 blastocysts represent nearly 5% of embryos in IVF with acceptable pregnancy and live birth rates, however data are still limited. Therefore, this study aimed to analyze the ongoing pregnancy rate (OPR) of D7 blastocysts in single euploid frozen embryo transfer (FET) cycles. An observational study was performed including 1527 FET cycles with blastocysts biopsied on D5 (N = 855), D6 (N = 636) and D7 (N = 36). Blastocysts were classified as good (AA/AB/BA), fair (BB) or poor (AC/BC/CC/CA/CB) (Gardner scoring). FETs were performed in natural cycles (NC) or hormone replacement therapy (HRT) cycles. Patient's age differed significantly between D5, D6 and D7 blastocysts FET cycles (33.2 ± 5.6, 34.4 ± 5.3 and 35.9 ± 5.2, P < 0.001). OPRs were higher when D5 euploid blastocysts were transferred compared with D6 and D7 (56.0% vs. 45.3% and 11.1%, P < 0.001). Poor quality blastocysts were predominant in D7 blastocyst FET cycles (good quality: 35.4%, 27.2%, 5.6%; fair quality: 52.1%, 38.5%, 11.1%; poor quality: 12.5%, 34.3%, 83.3%, P < 0.001 for D5, D6 and D7 blastocysts; respectively). OPR was significantly reduced by D7 blastocyst FETs (OR = 0.23 [0.08;0.62], P = 0.004), patient's BMI (OR = 0.96 [0.94;0.98], P < 0.001), HRT cycles (OR = 0.70 [0.56;0.88], P = 0.002) and poor quality blastocysts (OR = 0.33 [0.24;0.45], P < 0.001). OPR is significantly reduced with D7 compared with D5/D6 euploid blastocysts in FET cycles. The older the patient, the more likely they are to have an FET cycle with blastocysts biopsied on D7, therefore culturing embryos until D7 can be a strategy to increase OPR outcomes in patients ≥38 years.

Blastocyst Prediction of Day-3 Cleavage-Stage Embryos Using Machine Learning

Background: Embryo selection plays an important role in the success of in vitro fertilization (IVF). However, morphological embryo assessment has a number of limitations, including the time required, lack of accuracy, and inconsistency. This study determined whether a machine learning-based model could predict blastocyst formation using day-3 embryo images.

Methods: Day-3 embryo images from IVF/intracytoplasmic sperm injection (ICSI) cycles performed at My Duc Phu Nhuan Hospital between August 2018 and June 2019 were retrospectively analyzed to inform model development. Day-3 embryo images derived from two-pronuclear (2PN) zygotes with known blastocyst formation data were extracted from the CCM-iBIS time-lapse incubator (Astec, Japan) at 67 hours post ICSI, and labeled as blastocyst/non-blastocyst based on results at 116 hours post ICSI. Images were used as the input dataset to train (85%) and validate (15%) the convolutional neural network (CNN) model, then model accuracy was determined using the training and validation dataset. The performance of 13 experienced embryologists for predicting blastocyst formation based on 100 day-3 embryo images was also evaluated.

Results: A total of 1,135 images were allocated into training ([Formula: see text] 967) and validation ([Formula: see text] 168) sets, with an even distribution for blastocyst formation outcome. The accuracy of the final model for blastocyst formation was 97.72% in the training dataset and 76.19% in the validation dataset. The final model predicted blastocyst formation from day-3 embryo images in the validation dataset with an area under the curve of 0.75 (95% confidence interval [CI] 0.69–0.81). Embryologists predicted blastocyst formation with the accuracy of 70.07% (95% CI 68.12%–72.03%), sensitivity of 87.04% (95% CI 82.56%–91.52%), and specificity of 30.93% (95% CI 29.35%–32.51%).

Conclusions: The CNN-based machine learning model using day-3 embryo images predicted blastocyst formation more accurately than experienced embryologists. The CNN-based model is a potential tool to predict additional IVF outcomes.

Ionophore application for artificial oocyte activation and its potential effect on morphokinetics: a sibling oocyte study

PURPOSE

To evaluate whether ionophore application at the oocyte stage changes the morphokinetics of the associated embryos in cases of artificial oocyte activation.

METHODS

In a prospective sibling oocyte approach, 78 ICSI patients with suspected fertilization problems had half of their MII-oocytes treated with a ready-to-use ionophore (calcimycin) immediately following ICSI (study group). Untreated ICSI eggs served as the control group. Primary analyses focused on morphokinetic behavior and the presence of irregular cleavages. The rates of fertilization, utilization, pregnancy, and live birth rate were also evaluated.

RESULTS

Ionophore-treated oocytes showed a significantly earlier formation of pronuclei (t2PNa) and a better synchronized third cell cycle (s3) (P < .05). The rate of irregular cleavage was unaffected (P > .05). Ionophore treatment significantly improved the overall rates of fertilization (P < .01) and blastocyst utilization (P < .05).

CONCLUSION

Ionophore application does not negatively affect cleavage timing nor is it associated with irregular cleavage.

Inter-centre reliability in embryo grading across several IVF clinics is limited: implications for embryo selection

RESEARCH QUESTION

What is the intra- and inter-centre reliability in embryo grading performed according to the Istanbul Consensus across several IVF clinics?

DESIGN

Forty Day 3 embryos and 40 blastocysts were photographed on three focal planes. Senior and junior embryologists from 65 clinics were invited to grade them according to the Istanbul Consensus (Study Phase I). All participants then attended interactive training where a panel of experts graded the same embryos (Study Phase II). Finally, a second set of pictures was sent to both embryologists and experts for a blinded evaluation (Study Phase III). Intra-centre reliability was reported for Study Phase I as Cohen's kappa between senior and junior embryologists; inter-centre reliability was instead calculated between senior/junior embryologists and experts in Study Phase I versus III to outline improvements after training (i.e. upgrade of Cohen's kappa category according to Landis and Koch).

RESULTS

Thirty-six embryologists from 18 centres participated (28% participation rate). The intra-centre reliability was (i) substantial (0.63) for blastomere symmetry (range -0.02 to 1.0), (ii) substantial (0.72) for fragmentation (range 0.29-1.0), (iii) substantial (0.66) for blastocyst expansion (range 0.19-1.0), (iv) moderate (0.59) for inner cell mass quality (range 0.07-0.92), (v) moderate (0.56) for trophectoderm quality (range 0.01-0.97). The inter-centre reliability showed an overall improvement from Study Phase I to III, from fair (0.21-0.4) to moderate (0.41-0.6) for all parameters under analysis, except for blastomere fragmentation among senior embryologists, which was already moderate before training.

CONCLUSIONS

Intra-centre reliability was generally moderate/substantial, while inter-centre reliability was just fair. The interactive training improved it to moderate, hence this workflow was deemed helpful. The establishment of external quality assessment services (e.g. UK NEQAS) and the avant-garde of artificial intelligence might further improve the reliability of this key practice for embryo selection.

Evaluation of Pregnancy Outcomes of Vitrified-Warmed Blastocyst Transfer before and after Endometrial Receptivity Analysis in Identical Patients with Recurrent Implantation Failure

Background: The clinical value of personalized embryo transfer (pET) guided by the endometrial receptivity analysis (ERA) tests for recurrent implantation failure (RIF) cases is still unclear. The aim of this study is to clarify the efficacy of ERA leading to personalization of the day of embryo transfer (ET) in RIF patients.

Methods: A retrospective study was performed for 94 patients with RIF who underwent ERA between July 2015 and December 2019. Pregnancy outcomes in a previous vitrified-warmed blastocyst transfer (previous VBT) and a personalized vitrified-warmed blastocyst transfer (pVBT) in identical patients were compared. The details of each pVBT were further analyzed between patients in a non-displaced group, which indicated “receptive” cases in ERA results and those who were in the displaced group, which indicated “non-receptive” cases.

Results: When the pregnancy rate, both per patient and per transfer cycle, of previous VBT and pVBT were compared, a significant increase in pVBT was observed between the two methods (5.3% vs. 62.8%, 4.4% vs. 47.9%, respectively). The pregnancy rates, implantation rates, and clinical pregnancy rates of the first pVBT were significantly higher in the displaced group than the non-displaced group. The cumulative ongoing pregnancy rate of the displaced group tended to be higher compared to that of the non-displaced group in the first pVBT, although the difference was not statistically significant (51.0% vs. 31.1%, [Formula: see text] = 0.06).

Conclusions: Our study demonstrates that pVBT guided by ERA tests may improve pregnancy outcomes in RIF patients whose window of implantation (WOI) is displaced, and its effect may be more pronounced at the first pVBT. The displacement of WOI may be considered to be one of the causes of RIF, and its adjustment may contribute to the improvement of pregnancy outcomes in RIF patients.

An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study

RESEARCH QUESTION

The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology.

DESIGN

This retrospective cohort study included 212 patients who underwent single blastocyst transfer at IVI Valencia. A single image of each of 186 embryos was studied, and the protein profile was analysed in 81 samples of spent embryo culture medium from patients included in the preimplantation genetic testing programme. The information extracted from the analyses was used as input data for the ANN. The multilayer perceptron and the back-propagation learning method were used to train the ANN. Finally, predictive power was measured using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Three ANN architectures classified most of the embryos correctly as leading (LB+) or not leading (LB-) to a live birth: 100.0% for ANN1 (morphological variables and two proteins), 85.7% for ANN2 (morphological variables and seven proteins), and 83.3% for ANN3 (morphological variables and 25 proteins). The artificial intelligence model using information extracted from blastocyst image analysis and concentrations of interleukin-6 and matrix metalloproteinase-1 was able to predict live birth with an AUC of 1.0.

CONCLUSIONS

The model proposed in this preliminary report may provide a promising tool to select the embryo most likely to lead to a live birth in a euploid cohort. The accuracy of prediction demonstrated by this software may improve the efficacy of an assisted reproduction treatment by reducing the number of transfers per patient. Prospective studies are, however, needed.

Time-lapse imaging: the state of the art†

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

Development of automated annotation software for human embryo morphokinetics

STUDY QUESTION

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

SUMMARY ANSWER

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

WHAT IS KNOWN ALREADY

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

STUDY DESIGN, SIZE, DURATION

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

PARTICIPANTS/MATERIALS, SETTING, METHODS

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

MAIN RESULTS AND THE ROLE OF CHANCE

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

LIMITATIONS, REASONS FOR CAUTION

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

WIDER IMPLICATIONS OF THE FINDINGS

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

STUDY FUNDING/COMPETING INTEREST(S)

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

TRIAL REGISTRATION NUMBER

N/A

Altered morphokinetics in equine embryos from oocytes exposed to DEHP during IVM

Di-(2-ethylhexyl) phthalate (DEHP) is a commonly used plasticizer with endocrine-disrupting properties. In this study, we used an equine model to investigate DEHP concentrations in ovarian follicular fluid (FF), and to determine the effects of exposure of oocytes to potentially toxic concentrations of DEHP during in vitro maturation (IVM) on embryo development and quality. Embryo development was evaluated using time-lapse monitoring (TLM), a photomicroscopic tool that reveals abnormalities in cleavage kinetics unobservable by conventional morphology assessment. Blastocyst bioenergetic/oxidative status was assessed by confocal analysis. The possibility that verbascoside (VB), a bioactive polyphenol with antioxidant activity, could counteract DEHP-induced oocyte oxidative damage, was investigated. DEHP was detected in FF and in IVM media at concentrations up to 60 nM. Culture of oocytes in the presence of 500 nM DEHP delayed second polar body extrusion, reduced duration of the second cell cycle, and increased the percentage of embryos showing abrupt multiple cleavage, compared with controls. Mitochondrial activity and intracellular levels of reactive oxygen species were reduced in blastocysts from DEHP-exposed oocytes. VB addition during IVM limited DEHP-induced blastocyst damage. In conclusion, DEHP is detectable in equine FF and culture medium, and oocyte exposure to increased concentrations of DEHP during IVM affects preimplantation embryo development. Moreover, TLM, reported for the first time in the horse in this study, is an efficient tool for identifying altered morphokinetic parameters and cleavage abnormalities associated with exposure to toxic compounds.

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

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

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.

Time-lapse imaging systems in IVF laboratories: a French national survey

PURPOSE

Although the clinical value of time-lapse imaging (TLI) systems in in vitro fertilization (IVF) cycles is still debated, its prevalence worldwide seems to be expanding. The situation of TLI in the USA has been recently surveyed, but these results might not be transposable to other countries with different IVF regulation and funding such as France. This study evaluated the TLI situation in French IVF laboratories.

METHODS

An anonymous online cross-sectional survey was sent by email to 210 embryologists in September and October 2017. Laboratories, demographics, TLI clinical use, purchasing plan, and embryologists' opinions were analyzed using logistic regression to calculate odds ratio.

RESULTS

Of the 210 lab directors surveyed, 78 responded (37.1%), 43 (55%) working in private IVF laboratories and 35 (45%) in public hospitals. Thirty (38.5%) were TLI users. The odds of TLI possession were not statistically different according to laboratory sector or size. Most embryologists (n = 21, 70%) used TLI for unselected patients. Cost was the main reason given by non-users for not implementing TLI (n = 24, 50%). Most respondents were convinced that TLI is superior to standard morphology (n = 52, 73.2%) and that TLI improves culture conditions (n = 62, 84.9%). However, half (n = 39, 54.9%) indicated that evidence was still lacking to assert TLI clinical usefulness.

CONCLUSION

The prevalence of TLI systems and embryologists' opinion in France was slightly different from the American situation. The different regulation and funding policy might account for some differences in terms of TLI use and perception.

Morphology vs morphokinetics: a retrospective comparison of inter-observer and intra-observer agreement between embryologists on blastocysts with known implantation outcome

Objective

Our primary aim was to compare the morphology and morphokinetics on inter- and intra-observer agreement for blastocyst with known implantation outcome. Our secondary aim was to validate the morphokinetic parameters' ability to predict pregnancy using a previous published selection algorithm, and to compare this to standard morphology assessments.

Methods

Two embryologists made independent blinded annotations on two occasions using time-lapse images and morphology evaluations using the Gardner Schoolcraft criteria of 99 blastocysts with known implantation outcome. Inter- and intra-observer agreement was calculated and compared using the two methods. The embryos were grouped based on their morphological score, and on their morphokinetic class using a previous published selection algorithm. The implantation rates for each group was calculated and compared.

Results

There was moderate agreement for morphology, with agreement on the same embryo score in 55 of 99 cases. The highest agreement rate was found for expansion grade, followed by trophectoderm and inner cell mass. Correlation with pregnancy was inconclusive. For morphokinetics, almost perfect agreement was found for early and late embryo development events, and strong agreement for day-2 and day-3 events. When applying the selection algorithm, the embryo distributions were uneven, and correlation to pregnancy was inconclusive.

Conclusions

Time-lapse annotation is consistent and accurate, but our external validation of a previously published selection algorithm was unsuccessful.

Critical appraisal of the Vienna consensus: performance indicators for assisted reproductive technology laboratories

The Vienna consensus, based on the recommendations of an expert panel, has identified 19 performance indicators for assisted reproductive technology (ART) laboratories. Two levels of reference values are established for these performance indicators: competence and benchmark. For over 10 years, the Spanish embryology association (ASEBIR) has participated in the definition and design of ART performance indicators, seeking to establish specific guidelines for ART laboratories to enhance quality, safety and patient welfare. Four years ago, ASEBIR took part in an initiative by AENOR, the Spanish Association for Standardization and Certification, to develop a national standard in this field (UNE 17900:2013 System of quality management for assisted reproduction laboratories), extending the former requirements, based on ISO 9001, to include performance indicators. Considering the experience acquired, we discuss various aspects of the Vienna consensus and consider certain discrepancies in performance indicators between the consensus and UNE 179007:2013, and analyse the definitions, methodology and reference values used.