  Assessing and selecting the most viable embryos for transfer is an essential
part of in vitro fertilization (IVF). In recent years, several approaches have
been made to improve and automate the procedure using artificial intelligence
(AI) and deep learning. Based on images of embryos with known implantation data
(KID), AI models have been trained to automatically score embryos related to
their chance of achieving a successful implantation. However, as of now, only
limited research has been conducted to evaluate how embryo selection models
generalize to new clinics and how they perform in subgroup analyses across
various conditions. In this paper, we investigate how a deep learning-based
embryo selection model using only time-lapse image sequences performs across
different patient ages and clinical conditions, and how it correlates with
traditional morphokinetic parameters. The model was trained and evaluated based
on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which
14,644 embryos were transferred KID embryos. In an independent test set, the AI
model sorted KID embryos with an area under the curve (AUC) of a receiver
operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A
clinic hold-out test showed that the model generalized to new clinics with an
AUC range of 0.60-0.75 for KID embryos. Across different subgroups of age,
insemination method, incubation time, and transfer protocol, the AUC ranged
between 0.63 and 0.69. Furthermore, model predictions correlated positively
with blastocyst grading and negatively with direct cleavages. The fully
automated iDAScore v1.0 model was shown to perform at least as good as a
state-of-the-art manual embryo selection model. Moreover, full automatization
of embryo scoring implies fewer manual evaluations and eliminates biases due to
inter- and intraobserver variation.
