A Combination of Artificial Intelligence with Genetic Algorithms on Static Time-Lapse Images Improves Consistency in Blastocyst Assessment, An Interpretable Tool to Automate Human Embryo Evaluation: A Retrospective Cohort Study

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.