Artificial Intelligence-Empowered Embryo Selection for IVF Applications: A Methodological Review - Lazaros Moysis; Lazaros Alexios Iliadis; George Vergos; Sotirios P. Sotiroudis; Achilles D. Boursianis; Achilleas Papatheodorou; Konstantinos-Iraklis D. Kokkinidis; Mohammad Abdul Matin; Panagiotis Sarigiannidis; Ilias Siniosoglou; Vasileios Argyriou; Sotirios K. Goudos (Machine Learning and Knowledge Extraction)
In vitro fertilization (IVF) is a well-established and efficient assisted reproductive technology (ART). However, it requires a series of costly and non-trivial procedures, and the success rate still needs improvement. Thus, increasing the success rate, simplifying the process, and reducing costs are all essential challenges of IVF. These can be addressed by integrating artificial intelligence techniques, like deep learning (DL), with several aspects of the IVF process. DL techniques can help extract important features from the data, support decision making, and perform several other tasks, as architectures can be adapted to different problems. The emergence of AI in the medical field has seen a rise in DL-supported tools for embryo selection. In this work, recent advances in the use of AI and DL-based embryo selection for IVF are reviewed. The different architectures that have been considered so far for each task are presented. Furthermore, future challenges for artificial intelligence-based ARTs are outlined.