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Background: In recent times, various algorithms have been developed to assist in the selection of embryos fortransfer based on artificial intelligence (AI). Nevertheless, the majority of AI models employed in this context werecharacterized by a lack of transparency. To address these concerns, we aim to design an interpretable tool to automatehuman 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) imagestaken at 110 hours post-injection. All the images were evaluated by five embryologists from different clinics in termsof blastocyst expansion (BE), quality of the inner cell mass (ICM), and trophectoderm (TE). The embryo databasewas 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 Matlabsoftware. The resulting quantified variables were utilized in AI techniques (ANN and GA). Finally, the accuracy andperformance 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 wascompared with Kappa Index.Results: The overall agreement among embryologists was low (Kappa: 0.4 for BE; and 0.3 for TE and ICM). The AItool achieved higher consistency (Kappa 0.7 for BE and ICM; and 0.4 for TE). The AI exhibited high accuracy in classifyingBE (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 blastocystmorphology. This research represents an important step towards fully automating objective embryo evaluation.

Thoroughly analyzing the sperm and exploring the information obtained using artificial intelligence (AI) could be the key to improving fertility estimation. Artificial neural networks have already been applied to calculate zootechnical indices in animals and predict fertility in humans. This method of estimating the results of reproductive biotechnologies, such as in vitro embryo production (IVEP) in cattle, could be valuable for livestock production. This study was developed to model IVEP estimates in Senepol animals based on various sperm attributes, through retrospective data from 290 IVEP routines performed using 38 commercial doses of semen from Senepol bulls. All sperm samples that had undergone the same procedure during sperm selection for in vitro fertilization were evaluated using a computer-assisted sperm analysis (CASA) system to define sperm subpopulations. Sperm morphology was also analyzed in a wet preparation, and the integrity of the plasma and acrosomal membranes, mitochondrial potential, oxidative status, and chromatin resistance were evaluated using flow cytometry. A previous study identified three sperm subpopulations in such samples and the information used in tandem with other sperm quality variables to perform an AI analysis. AI analysis generated models that estimated IVEP based on the season, donor, percentage of viable oocytes, and 18 other sperm predictor variables. The accuracy of the results obtained for the three best AI models for predicting the IVEP was 90.7, 75.3, and 79.6%, respectively. Therefore, applying this AI technique would enable the estimation of high or low embryo production for individual bulls based on the sperm analysis information.

In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers to the three qualities of the blastocyst grading, contains a description of 24 variables that were extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classify the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40× magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4× zoom from the cell phone camera). The images obtained from the control and experimental groups were uploaded on Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) for automatic quality grade classification by the three ANNs of the Blasto3Q program. Adjustments on the software program through the use of scaling algorithm software were performed to ensure the proper search and segmentation of the embryo in the raw images when they were captured by the smartphone, since this source produced small embryo images compared with those from the inverted microscope. With this new program, 77.8% of the images from smartphones were successfully segmented and from those, 85.7% were evaluated by the Blasto3Q in agreement with the control group.