Introducing time-lapse for flexible embryo assessment in in vitro fertilization: implications for grading confidence and workflow efficiency

引入延时摄影技术进行体外受精中的灵活胚胎评估:对分级信心和工作流程效率的影响

阅读:1

Abstract

OBJECTIVE: To compare embryologists' confidence levels and grading concordance when assessing embryo morphology using time-lapse images vs. traditional direct observation under a microscope. DESIGN: Prospective study. SUBJECTS: Six embryologists on staff at the laboratory who ranged from junior to senior skillset levels. EXPOSURE: None. MAIN OUTCOME MEASURES: Embryologists' confidence levels in morphology assessments, intraobserver agreement for embryo grading, and assessment duration when using time-lapse images compared with direct observation under microscopes. RESULTS: From 6,435 morphology assessments across 714 embryos (62 cohorts), high-confidence levels were observed on day 5 for gradings using either direct observation (97.7%) or time-lapse images (94.6%), with no statistically significant difference. Confidence was consistently high (>85%) across days 1, 3, 6, and 7, with direct observation having higher confidence rate than time-lapse-based assessment on days 1 and 3. Intraobserver agreement between direct observation vs. time-lapse-based assessment showed good-to-excellent concordance (κ ≥ 0.60) except for blastocyst morphology grade (κ = 0.58). The highest agreement was observed for day 5-7 embryo disposition decisions, with 94.1% concordance (κ = 0.89). Assessment durations were comparable between the two grading methods. CONCLUSION: Embryologists maintained high confidence in morphology assessments using time-lapse systems, even under time constraints. Intraobserver agreement for embryo grading, assessed by comparing the same embryologist's gradings using time-lapse images vs. direct observation, was comparable to previously reported levels of agreement for traditional morphology gradings. These findings highlight the feasibility of flexible or remote assessments without disrupting embryo culture, enhancing workflow adaptability, and paving the way for advancements with artificial intelligence integration.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。