Image Processing Approach for Grading IVF Blastocyst: A State-of-the-Art Review and Future Perspective of Deep Learning-Based Models

Abstract

The development of intelligence-based methods and application systems has expanded for the use of quality blastocyst selection in in vitro fertilization (IVF). Significant models on assisted reproductive technology (ART) have been discovered, including ones that process morphological image approaches and extract attributes of blastocyst quality. In this study, (1) the state-of-the-art in ART is established using an automated deep learning approach, applications for grading blastocysts in IVF, and related image processing techniques. (2) Thirty final publications in IVF and deep learning were found by an extensive literature search from databases using several relevant sets of keywords based on papers published in full-text English articles between 2012 and 2022. This scoping review sparks fresh thought in deep learning-based automated blastocyst grading. (3) This scoping review introduces a novel notion in the realm of automated blastocyst grading utilizing deep learning applications, showing that these automated methods can frequently match or even outperform skilled embryologists in particular deep learning tasks. This review adds to our understanding of the procedure for selecting embryos that are suitable for implantation and offers important data for the creation of an automated computer-based system for grading blastocysts that applies deep learning.

Exploring the Role of Explainability in AI-Assisted Embryo Selection

“…A few discuss the biological aspects of the problem with particular depth [22], while others focus on the variety of tasks one can tackle (e.g., embryo segmentation, quality grading, etc.) [20]. The most ambitious tasks, those of predicting clinical pregnancy or fetal heartbeat from five days old fertilized embryo images, are reviewed in [23], together with the ploidy status (number of chromosomes) prediction task.…”