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.

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.

and E2 levels, and evaluated in terms of R-squared (R2) and mean absolute error (MAE). Together, the two models enabled the prediction of the number of MII eggs when triggering on the last day of stimulation compared to one day in the future (i.e. trigger "today vs. tomorrow").RESULTS: The RNN predicted next-day follicles with MAE¼2.51 follicles and next-day estradiol levels with MAE¼375 IUs (16% of E2 level), for a test dataset comprising 20% of the data. The MAEs for next-day perbin follicle counts were 1. 96, 1.88, 1.36, 1.17, .89, and .60 follicles for bins <¼11mm, 12-14mm, 15-16mm, 17-18mm, 19-20mm, and >20mm, respectively. Multiple linear regression with recursive feature elimination identified baseline AFC, E2, and follicles <¼11mm, 12-14mm, 15-16mm, 17-18mm, and 19-20mm as significant predictors of MII eggs retrieved, and predicted MII eggs with R2¼0.62 and MAE¼3.11 eggs. When combining follicle and E2 forecasting with the linear model, the integrated approach predicted that 35% cycles may have resulted in more MII eggs if trigger had occurred one day later.CONCLUSIONS: We developed a machine learning approach for optimizing the day of trigger during ovarian stimulation. Our approach combined an RNN to forecast E2 and follicle counts with an interpretable linear model to predict the number of MII eggs retrieved. These models predicted that a significant number of cycles may have resulted in more MII eggs if trigger had occurred one day later. We acknowledge that for some cases, earlier trigger may have been chosen to prevent the risk of adverse clinical outcomes, which will be investigated in future work.IMPACT STATEMENT: We have developed a machine learning approach for forecasting E2 and follicle growth and predicting the number of MII eggs retrieved during ovarian stimulation, which may help with the decision of triggering "today vs. tomorrow".

Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to inter- and intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. 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. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).