Abstract
PGT-A testing is time consuming and expensive. Recent AI models have been used to predict ploidy status using embryo images or time-lapse videos with area under the curve (AUC) between 0.62 and 0.75, indicating reasonable but not perfect predictions. AI models thus cannot replace PGT-A entirely, but may help select which blastocysts to test in order to achieve a desired number of euploids. In this study, we investigated if AI models could be used to prioritize and reduce the number of blastocysts requiring PGT-A testing to attain at least one euploid blastocyst per treatment.
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