Testing an artificial intelligence algorithm to predict fetal heartbeat of vitrified-warmed blastocysts from a single image: predictive ability in different settings

• Published in: Human reproduction (Oxford) 2024-10, Vol.39 (10), p.2240-2248
• Main Authors: Conversa, L, Bori, L, Insua, F, Marqueño, S, Cobo, A, Meseguer, M
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Artificial Intelligence; Blastocyst - physiology; Cryopreservation - methods; Embryo Culture Techniques; Embryo Transfer - methods; Female; Heart Rate, Fetal - physiology; Humans; Pregnancy; Retrospective Studies; Time-Lapse Imaging - methods; Vitrification
• ISSN: 0268-1161; 1460-2350; 1460-2350
• DOI: 10.1093/humrep/deae178

Abstract STUDY QUESTION Could an artificial intelligence (AI) algorithm predict fetal heartbeat from images of vitrified-warmed embryos? SUMMARY ANSWER Applying AI to vitrified-warmed blastocysts may help predict which ones will result in implantation failure early enough to thaw another. WHAT IS KNOWN ALREADY The application of AI in the field of embryology has already proven effective in assessing the quality of fresh embryos. Therefore, it could also be useful to predict the outcome of frozen embryo transfers, some of which do not recover their pre-vitrification volume, collapse, or degenerate after warming without prior evidence. STUDY DESIGN, SIZE, DURATION This retrospective cohort study included 1109 embryos from 792 patients. Of these, 568 were vitrified blastocysts cultured in time-lapse systems in the period between warming and transfer, from February 2022 to July 2023. The other 541 were fresh-transferred blastocysts serving as controls. PARTICIPANTS/MATERIALS, SETTING, METHODS Four types of time-lapse images were collected: last frame of development of 541 fresh-transferred blastocysts (FTi), last frame of 467 blastocysts to be vitrified (PVi), first frame post-warming of 568 vitrified embryos (PW1i), and last frame post-warming of 568 vitrified embryos (PW2i). After providing the images to the AI algorithm, the returned scores were compared with the conventional morphology and fetal heartbeat outcomes of the transferred embryos (n = 1098). The contribution of the AI score to fetal heartbeat was analyzed by multivariate logistic regression in different patient populations, and the predictive ability of the models was measured by calculating the area under the receiver-operating characteristic curve (ROC-AUC). MAIN RESULTS AND THE ROLE OF CHANCE Fetal heartbeat rate was related to AI score from FTi (P