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Background: In recent times, various algorithms have been developed to assist in the selection of embryos for transfer based on artificial intelligence (AI). Nevertheless, the majority of AI models employed in this context were characterized by a lack of transparency. To address these concerns, we aim to design an interpretable tool to automate human embryo evaluation by combining artificial neural networks (ANNs) and genetic algorithms (GA). Materials and Methods: This retrospective cohort study included 223 human blastocyst time-lapse (TL) images taken at 110 hours post-injection. All the images were evaluated by five embryologists from different clinics in terms of blastocyst expansion (BE), quality of the inner cell mass (ICM), and trophectoderm (TE). The embryo database was used to develop an AI system (70% training, 15% validation, and 15% test) for automate blastocyst assessment. The entire set of images underwent a standardization process, followed by processing and segmentation using Matlab software. The resulting quantified variables were utilized in AI techniques (ANN and GA). Finally, the accuracy and performance of the automation tool was assessed with the area under the receiver operating characteristic (ROC) curve (AUC). Then, the level of agreement among embryologists and between embryologists and the AI system was compared with Kappa Index. Results: The overall agreement among embryologists was low (Kappa: 0.4 for BE; and 0.3 for TE and ICM). The AI tool achieved higher consistency (Kappa 0.7 for BE and ICM; and 0.4 for TE). The AI exhibited high accuracy in classifying BE (test 81.5%), ICM (test 78.8%), and TE (test 78.3%) and better performance for BE (AUC 0.888-0.956) than for ICM (AUC 0.605-0.854) and TE (AUC 0.726-0.769) assessment. Conclusion: Our AI tool highlighted the superior consistency of AI compared to human operators in grading blasto-cyst morphology. This research represents an important step towards fully automating objective embryo evaluation. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Does culture in a high relative humidity atmosphere improve clinical outcomes when using a time-lapse integrated incubator and single-step culture medium? SUMMARY ANSWER Using an integrated time-lapse system and single-step culture medium, culture in a high relative humidity atmosphere increases the likelihood of embryos, especially those subjected to preimplantation genetic testing for aneuploidies, to achieve a pregnancy compared to those cultured in dry conditions. WHAT IS KNOWN ALREADY The use of a humid atmosphere inside incubators can reduce changes in culture media osmolality, which has been reported to have a significant effect on embryo quality and morphokinetics. Studies assessing the effect of humid culture (HC) in clinical outcomes are, however, scarce and inconclusive, mostly due to a high variability in culture conditions and reduced sample size. STUDY DESIGN, SIZE, DURATION Retrospective cohort study performed over 1627 ICSI cycles performed during 3 consecutive years in which embryo cohorts were cultured in a time-lapse incubator with three dry and three humidified chambers, and using single-step culture medium. Clinical outcomes were compared between treatments in which embryo cohorts were cultured in either humid (n = 833) or dry (n = 794) conditions. PARTICIPANTS/MATERIALS, SETTING, METHODS The study includes autologous treatments, with (N = 492) and without (N = 372) preimplantation genetic testing for aneuploidies (PGT-A) and ovum donation treatments (N = 763), performed in three university-affiliated private IVF centres. Stimulation, oocyte pickup and fertilization were performed according to the standard procedures of the clinic. All embryo cohorts were cultured in the same model of time-lapse incubator, distributed to either a dry or humidified chamber, while the rest of the culture variables remained equal. The population was weighted by the inverse probability of treatment to control for all measured confounders. The association between HC and the main outcome was assessed by logistic regression over the weighted population. The E- value was reported as a way of considering for unmeasured confounders. Differences in embryo development and other secondary outcomes between the study groups were assessed by Pearson Chi-squared test, ANOVA test and Kaplan–Meier survival analysis. MAIN RESULTS AND THE ROLE OF CHANCE An univariable logistic regression analysis, weighted by the inverse probability of treatment, determined that embryos cultured in humid conditions are more likely to achieve a clinical pregnancy than those cultured in dry conditions (odds ratio (OR) = 1.236 (95% CI 1.009–1.515), P  =   0.041, E  = 1.460). Through stratification, it was determined that said effect is dependent on the type of treatment: no improvement in clinical pregnancy was present in ovum donation or autologous treatments, but a statistically significant positive effect was present in treatments with preimplantation genetic testing (OR = 1.699 (95% CI 1.084–2.663), P =  0.021, E  =   1.930). Said increase does not relate with an improvement in later outcomes. Differences were also found in variables related to embryo developmental morphokinetics. LIMITATIONS, REASONS FOR CAUTION The retrospective nature of the study makes it susceptible to some bias linked to the characteristics of the treatments. To lessen the effect of possible biases, cases were weighted by the inverse probability of treatment prior to the evaluation of the outcome, as means to assess for measured confounders. In addition, the E -value of the weighted OR was calculated as a sensitivity analysis for unmeasured confounders. A randomized prospective study could be performed for further assessing the effect of humid conditions in clinical outcome. WIDER IMPLICATIONS OF THE FINDINGS These results support that embryo culture under conditions of high relative humidity contributes to optimize clinical results in undisturbed culture in a time-lapse incubator with single-step medium. To our knowledge, this is the largest study on the matter and the first performing a propensity score-based analysis. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the "Centro para el Desarrollo Tecnologico Industrial" from the Spanish Ministry of Science, Innovation, and Universities (CDTI-20170310) and Generalitat Valenciana and European Social Fund (ACIF/2019/264). None of the authors have any competing interest to declare. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]