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Introducing time-lapse for flexible embryo assessment in in vitro fertilization: implications for grading confidence
Alicia Picou1, Kelly Athayde Wirka2, Allison Baxter Catherino2
1IVF Laboratory, Ovation Fertility, Austin, Texas.
F&S Reports
|July 7, 2025
Summary
Embryologists show high confidence and comparable grading accuracy using time-lapse imaging versus direct observation for embryo assessment. This supports flexible workflows and potential AI integration in fertility labs.
Area of Science:
- Reproductive Medicine
- Embryology
- In Vitro Fertilization (IVF)
Background:
- Embryo morphology assessment is crucial for IVF success.
- Traditional methods rely on direct microscopic observation.
- Time-lapse imaging (TLI) offers continuous monitoring but its impact on embryologist confidence and grading concordance needs evaluation.
Purpose of the Study:
- To compare embryologists' confidence levels and grading concordance when assessing embryo morphology using TLI versus traditional direct observation.
- To evaluate assessment duration between the two methods.
Main Methods:
- Prospective study involving six embryologists with varying skill levels.
- Morphology assessments were performed using both direct observation and TLI.
- Data collected included confidence levels, intraobserver agreement (using Cohen's kappa), and assessment duration.
Main Results:
- High confidence levels (>85%) were maintained for both methods across multiple days.
- Intraobserver agreement showed good-to-excellent concordance (κ ≥ 0.60) for most assessments, with slight variation for blastocyst grading.
- Concordance was highest for day 5-7 embryo disposition decisions (κ = 0.89).
- Assessment durations were comparable between TLI and direct observation.
Conclusions:
- Embryologists exhibit high confidence in embryo morphology assessments using TLI.
- TLI demonstrates comparable intraobserver agreement to traditional methods.
- Findings support the feasibility of flexible or remote assessments, enhancing workflow adaptability and paving the way for AI integration in embryology.
