Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image

PURPOSE: To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. METHODS: A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI‐based method with 5‐fold cross‐validation retrospectively for classifying embryos. RESULTS: The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth (P < 0.005). CONCLUSIONS: Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.