Document Type : Original Article
Authors
1
IVIRMA Global Research Alliance, IVIRMA Roma, Italy.
2
IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain.
3
Universidade Estadual Paulista (Unesp), Faculdade de Ciências e Letras, Câmpus de Assis SP, Brazil.
4
Aria Fertility, London, UK.
5
Ronald O Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, NY, USA.
6
Associate Professor, Embryology Lab Director at Weill Cornell Medicine New York, New York
7
IVIRMA Global Research Alliance, IVIRMA Valencia, Spain.
10.22074/ijfs.2024.2008339.1510
Abstract
Background: In recent times, various algorithms have been developed to assist in the selection of embryos for transfer based on artificial intelligence (AI). Nevertheless, the majority of AI models employed in this context were characterized by a lack of transparency. To address these concerns, we aim to design an interpretable tool to automate human 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 images taken at 110 hours post-injection. All the images were evaluated by five embryologists from different clinics in terms of blastocyst expansion (BE), quality of the inner cell mass (ICM), and trophectoderm (TE). The embryo database was 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 Matlab software. The resulting quantified variables were utilized in AI techniques (ANN and GA). Finally, the accuracy and performance 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 was compared with Kappa Index.
Results: The overall agreement among embryologists was low (Kappa: 0.4 for BE; and 0.3 for TE and ICM). The AI tool achieved higher consistency (Kappa 0.7 for BE and ICM; and 0.4 for TE). The AI exhibited high accuracy in classifying BE (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 trophectoderm (AUC 0.726-0.769) assessment.
Conclusion: Our AI tool highlighted the superior consistency of AI compared to human operators in grading blastocyst morphology. This research represents an important step towards fully automating objective embryo evaluation.
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