Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.

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OBJECTIVE: To study the application of image processing for segmentation of blastocysts images and extraction of potential variables for prediction of embryo fitness. DESIGN: Retrospective study. SETTING: Single reproductive medical center. IVI-RMA (Valencia, Spain) between 2017 and 2019. PATIENTS: An initial dataset including 353 images from EmbryoScope and 474 images from Geri incubators was acquired, of which 320 images from EmbryoScope and 309 images from Geri incubators were used in this study. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Successful segmentation of images into trophectoderm (TE), blastocoel, and inner cell mass (ICM) using the proposed processing steps. RESULTS: A total of 33 variables were automatically generated by digital image processing, each representing a different aspect of the embryo and describing a different characteristic of the expanding blastocyst (EX), ICM, or TE. 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. CONCLUSIONS: The proposed image processing protocol that can successfully segment human blastocyst images from two distinct sources and extract 33 variables with potential utility in embryo selection for ART.

Study question Does the post-warmed blastocyst dynamics have an impact over the likelihood of achieving a live birth? Summary answer Variables related to dynamics of vitrified/warmed blastocysts have shown a greater effect on the live birth prediction than only embryo morphological quality through artificial intelligence. What is known already Morphological dynamics of vitrified/warmed blastocysts were described by Coello et al., in 2017. The investigated markers were the thickness of zona pellucida (µm) and blastocysts area (µm2) after warming and before transfer, the area of the inner cell mass (µm2), time of initiation of reexpansion (in minutes), and presence of collapse or contraction. They found a correlation between blastocyst reexpansion and implantation rate and developed a hierarchical model for implantation prediction. In our study, we evaluated the post-warmed blastocyst dynamics for live birth prediction by using novel artificial intelligence techniques. Study design, size, duration This retrospective analysis included 415 vitrified/warmed blastocysts with known live birth data. Blastocysts after warming were placed in EmbryoScope (Vitrolife) immediately until embryo transfer. Embryo evaluation and selection were performed by senior embryologists according to fresh blastocyst morphology (before vitrification). Then, parameters related to post-warmed blastocyst dynamics were calculated. Finally, these variables and the embryo morphological grade before the vitrification were used as input data for ANNs optimized with genetic algorithm for live birth prediction. Participants/materials, setting, methods Blastocysts were vitrified and warmed by the Cryotop method (Kitazato,Biopharma). During the period between the warming procedure and the embryo transfer, the following variables were measured with the drawing tools provided by the EmbryoViewer workstation: zona pellucida thinning (µm), blastocyst expansion (um) and the speed of these two events (µm/h). Finally, multilayer perceptron neural networks were trained with data of 331 embryos by using the backpropagation learning algorithm and tested with data of 84 embryos. Main results and the role of chance We trained and tested three architectures of ANNs with different input variables as follows: post-warmed variables (thinning of the zona pellucida, blastocyst expansion, thinning speed and expansion speed) and morphological grade (A, B or C) for ANN1, only post-warmed variables for ANN2 and only morphological grade for ANN3. The highest success rate when ANNs classified embryos as positive and negative live birth (LB+ and LB-) was achieved by combining post-warmed variables and morphological grade before embryo vitrification. The general accuracies for the blind tests were: 73.8% for ANN1, 66.7% for ANN2 and 71.4% for ANN3. Likewise, this combination achieved the highest AUC on test dataset to predict LB- (0.76 for ANN1, 0.74 for ANN2 and 0.67 for ANN3). However, the ANN2 trained with only post-warmed variables showed the best capacity to predict LB+ with an AUC of 0.73 (versus 0.46 for ANN1 and 0.5 for ANN3). Limitations, reasons for caution The main limitation is the subjectivity of manual annotations, although only one embryologist participated in this task. Wider implications of the findings: The dynamics of vitrified/warmed blastocysts prior to embryo transfer could be more relevant variables than the morphological quality on day 5 before the cryopreservation. The analysis of embryo behavior after warming could improve clinical outcomes in frozen embryo transfers. Trial registration number none

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