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.

Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra-and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.

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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).

OBJECTIVE: To assess the performance in a clinical setting of IVFvision.ai, an artificial intelligence (AI) system for blastocyst selection DESIGN: Retrospective analysis of single blastocyst transfer (SBT) images MATERIALS AND METHODS: Convolutional Neural Networks were used to develop IVFvision.ai, an algorithm that differentiates between Day-5 blastocysts with a positive or negative implantation outcome. Implantation was confirmed by the presence of an embryonic sac with heartbeat.External validation of IVFvision.ai was performed at a University IVF Clinic using 113 anonymised Embryoscope images of SBT. Assessed images were taken at 116AEh hours post-insemination at the equatorial focal plane.The predictive ability and reliability of IVFvision.ai to correctly classify blastocysts according to implantation outcome were compared to the KID-ScoreD5 v2 prediction algorithm, as well as three expert Clinical Embryologists.AUC for each predictor was estimated using ROC curve analysis. Sensitivity, specificity, PPV, NPV and accuracy were calculated using crosstabs. Reliability of IVFvision.ai and embryologist assessments was calculated by the Interclass correlation coefficient (ICC). Stepwise logistic regression was used to model predictors significantly associated with implantation controlling for maternal age and fertilisation method.RESULTS: IVFvision.ai had higher AUC and overall accuracy in predicting implantation compared to KIDScoreD5 and all embryologists (Table 1). The reliability of IVFvision.ai was perfect (ICC ¼ 1.00, 95% CI 1.00 to 1.00), consistently returning the same classification after a triple reading process. The reliability between 3 embryologists was moderate (ICC ¼ 0.744, 95% CI 0.606 to 0.838), significantly lower than that of IVFvision.ai.Stepwise logistic regression showed that only IVFvision.ai prediction (p¼0.008) and fertilisation method (p¼0.051) were significantly associated with implantation. The combined model IVFvision.ai+Fert had an AUC 0.740. CONCLUSIONS: IVFvision.ai is a comprehensive AI system that identifies implantation outcome with high ability, outperforming all human experts and the KIDScore prediction algorithm. The system paves the way for clinical trials to improve the accuracy and efficiency of embryo selection in IVF.SUPPORT: None