Deep Learning Methods Enhancing IVF Success Through Embryo Selection and Follicular Analysis

Infertility patients have risen for these current
generations all over the world and have become one of the
biggest problems in the past ten years. This paper aims to
provide an overview of recently developed systems based on deep
learning techniques using different medical imaging modalities.
A three-dimensional (3D) ultrasound exaggeration is the most
innovative way for follicle monitoring, but 3D ultrasound follicle
monitoring has noticeable inter- and intra-observer variability in
the measurement of follicle diameter. The objective of this study
was to propose a novel deep learning-based automated model for
perfect 3D ultrasound follicles to check acceptable or approved
reliability and repeatability using deep learning techniques and
provide insights on well-known data sets used to train these
networks. Deep learning's potential to improve embryo selection
for implantation in IVF procedures seems promising, addressing
the limitations of traditional methods. By categorizing recent
works and discussing performance measures, the paper seems
geared towards providing insights for experts and technicians,
which could be beneficial for combating challenges associated
with infertility. Morphological assessment based on visual
inspection does have its limitations, especially in complex
processes like embryo selection for IVF. Human observation can
be subjective and prone to variability, leading to suboptimal
outcomes. Moreover, advancements in technology, particularly
the combination of deep learning and artificial intelligence, offer
promising avenues to overcome these limitations.