How should the best human embryo in vitro be? Current and future challenges for embryo selection.

In-vitro fertilization (IVF) aims at overcoming the causes of infertility and lead to a healthy live birth. To maximize IVF efficiency, it is critical to identify and transfer the most competent embryo within a cohort produced by a couple during a cycle. Conventional static embryo morphological assessment involves sequential observations under a light microscope at specific timepoints. The introduction of time-lapse technology enhanced morphological evaluation via the continuous monitoring of embryo preimplantation in vitro development, thereby unveiling features otherwise undetectable via multiple static assessments. Although an association exists, blastocyst morphology poorly predicts chromosomal competence. In fact, the only reliable approach currently available to diagnose the embryonic karyotype is trophectoderm biopsy and comprehensive chromosome testing to assess non-mosaic aneuploidies, namely preimplantation genetic testing for aneuploidies (PGT-A). Lately, the focus is shifting towards the fine-tuning of non-invasive technologies, such as "omic" analyses of waste products of IVF (e.g., spent culture media) and/or artificial intelligence-powered morphologic/morphodynamic evaluations. This review summarizes the main tools currently available to assess (or predict) embryo developmental, chromosomal, and reproductive competence, their strengths, the limitations, and the most probable future challenges.

Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles.

Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value.