During in vitro fertilization (IVF) cycles, multiple mature oocytes are retrieved from the ovary and are fertilized in the lab. The newly generated embryos can be transferred into the uterus on day-3,-4, or-5 of incubation, cryopreserved for subsequent transfers or discarded. Lacking a reliable noninvasive evaluation method of the potential to implant, pregnancy rates can be improved by cotransferring multiple embryos thus introducing health risks that are associated with multiple pregnancies. [1] Hence, the evaluation of embryo quality is required for improving live birth rates while minimizing medical complications and shortening time to pregnancy. [2-6] Machine learning was used for assessing the potential of embryos to blastulate [7,8] and to implant [9-11] based on manually annotated morphological and/or morphokientic features. Deep learning, which offers a powerful toolbox for carrying out automated and standardized classification tasks

In vitro fertilization is typically associated with high failure rates per transfer, leading to an acute need for the identification of embryos with high developmental potential. Current methods are tailored to specific times after fertilization, often require expert inspection, and have low predictive power. Automatic methods are challenged by ambiguous labels, clinical heterogeneity, and the inability to utilize multiple developmental points. In this work, we propose a novel method that trains a classifier conditioned on the time since fertilization. This classifier is then integrated over time and its output is used to assign soft labels to pairs of samples. The classifier obtained by training on these soft labels presents a significant improvement in accuracy, even as early as 30 h post-fertilization. By integrating the classification scores, the predictive power is further improved. Our results are superior to previously reported methods, including the commercial KIDScore-D3 system, and a group of eight senior professionals, in classifying multiple groups of favorable embryos into groups defined as less favorable based on implantation outcomes, expert decisions based on developmental trajectories, and/or genetic tests.