Artificial intelligence with Attention Branch Network and deep learning can predict live births by using time-lapse imaging of embryos after in vitro fertilisation

Abstract Research Question Can artificial intelligence (AI) improve the prediction of live births based on embryo images? Design The AI system was created by using the Attention Branch Network associated with deep learning to predict the probability of live birth from 141,444 images recorded by time-lapse imaging of 470 transferred embryos, of which 91 resulted in live birth and 379 resulted in non-live birth. The possibility that the calculated confidence scores of each embryo and the focused areas visualised in each embryo image can help predict subsequent live birth was examined. Results The AI system successfully visualised embryo features in focused areas that had potential to distinguish between live and non-live births for the first time. We observed no visual feature of embryos that was associated with live or non-live births, although there were many images in which high-focused areas existed around the zona pellucida. When a cut-off level for the confidence score was set at 0.341, the live birth rate was significantly greater for embryos with a score higher than the cut-off level than for those with a score lower than the cut-off level (P Conclusions We created an AI system with the confidence score that is useful for non-invasive selection of embryos that could result in live birth. Further study is necessary to improve selection accuracy.