Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction

Abstract

This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic testing in in vitro fertilization procedures. The current gold standard, preimplantation genetic testing for aneuploidy, has limitations, such as an invasive biopsy, financial burden, delays in results reporting, and difficulty in results reporting, Noninvasive ploidy screening methods, including blastocoel fluid sampling, spent media testing, and AI algorithms using embryonic images and clinical parameters, are explored. Various AI models have been developed using different machine learning algorithms, such as random forest classifier and logistic regression, have shown variable performance in predicting euploidy. Static embryo imaging combined with AI algorithms have demonstrated good accuracy in ploidy prediction, with models such as Embryo Ranking Intelligent Classification Algorithm and STORK-A outperforming human grading. Time-lapse embryo imaging analyzed by AI algorithms has also shown promise in predicting ploidy status; however, the inclusion of clinical parameters is crucial to improving the predictive value of these models. Mosaicism, an important aspect of embryo classification, is often overlooked in AI algorithms and should be considered in future studies. The integration of AI algorithms into microscopy equipment and Embryoscope platforms will facilitate noninvasive genetic testing. Further development of algorithms that optimize clinical considerations and incorporate minimal-necessary covariates will also enhance the predictive value of AI in embryo selection. Artificial intelligence-based ploidy prediction has the potential to improve pregnancy rates and reduce costs in in vitro fertilization cycles.

Keywords: Artificial intelligence; assisted reproduction; machine learning; noninvasive genetic screening; time-lapse imaging.