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Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning

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Document pages: 4 pages

Abstract: Embryo quality assessment after in vitro fertilization (IVF) is primarilydone visually by embryologists. Variability among assessors, however, remainsone of the main causes of the low success rate of IVF. This study aims todevelop an automated embryo assessment based on a deep learning model. Thisstudy includes a total of 1084 images from 1226 embryos. The images werecaptured by an inverted microscope at day 3 after fertilization. The imageswere labelled based on Veeck criteria that differentiate embryos to grade 1 to5 based on the size of the blastomere and the grade of fragmentation. Our deeplearning grading results were compared to the grading results from trainedembryologists to evaluate the model performance. Our best model fromfine-tuning a pre-trained ResNet50 on the dataset results in 91.79 accuracy.The model presented could be developed into an automated embryo assessmentmethod in point-of-care settings.

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