A review on automatic analysis of human embryo microscope images

Area of Science:

• Reproductive Medicine
• Biomedical Engineering
• Computational Biology

Background:

• In vitro fertilization (IVF) efficiency remains suboptimal despite 30 years of advancement.
• Accurate identification of viable embryos is crucial for successful pregnancy outcomes.
• Current embryo viability assessments are imprecise, often leading to multiple embryo transfers and associated risks.

Purpose of the Study:

• To review existing embryo scoring systems and recent advancements in automated embryo image analysis.
• To explore the potential of automated morphological feature analysis for precise embryo grading.
• To discuss challenges and future directions in developing objective embryo assessment tools.

Main Methods:

• Review of current embryo scoring systems based on morphological criteria.
• Survey of research on automated embryo image analysis techniques.
• Analysis of automated quantification of embryo characteristics and observer variation.

Main Results:

• Existing scoring systems rely heavily on subjective visual assessment.
• Automated analysis promises more objective quantification of embryo morphology.
• Image analysis can potentially eliminate inter- and intra-observer variability in embryo grading.

Conclusions:

• Automated embryo image analysis holds significant potential for improving IVF success rates.
• Objective grading of embryo quality can lead to more accurate selection and fewer embryo transfers.
• Further research is needed to overcome challenges and establish reliable automated systems for clinical use.