Back to list
A MORPHOLOGY-BASED EMBRYO INDEX IMPROVES CONSISTENCY IN EMBRYO GRADING AND PREDICTION OF PREGNANCY OUTCOMES
Oct 28, 2025
원문 보기
Authors
Hyejun Lee, Hyung Min Kim, Chaeyoon Lee, Mikyung Chung, Heekyung Hwang and Hyun Nyung Jo
Conferences
ASRM
ABSTRACT

OBJECTIVE:

To address inter-observer variability in Gardner embryo grading and investigate whether a newly defined set of morphological indicators can improve grading consensus and better predict clinical outcomes defined by fetal heart tone (FHT).

MATERIALS AND METHODS:

180 static day-5 embryo images were collected from three IVF clinics between 2015 and 2021. Three embryologists with over 5 years of experience independently assigned Gardner grades [INITIAL]. Consensus, defined as unanimous agreement among graders, was achieved in 53 cases (29.4%). A

morphology-based grading index composed of 10 features: 5 representing inner cell mass (ICM) and 5 representing trophectoderm (TE) characteristics. Each image was labeled accordingly, and final consensus grades [FINAL] were assigned. Ordinal logistic regression identified morphological factors for ICM and TE grades, and logistic regression evaluated FHT prediction. Model fit was assessed using Pseudo R².

RESULTS:

The index-based model showed greater explanatory power (Pseudo R² = 28.9%) for FHT compared to the FINAL Gardner model (10.4%), despite both showing borderline significance (p = 0.054 vs. p = 0.073). This limited statistical significance may be attributed to the relatively small sample size (n = 92) of FHT-matched images available for analysis. As shown in Table 1, the associations between the index features and assigned Gardner grades align with findings from previous studies.

CONCLUSIONS:

Our morphology-based index, derived from expert consensus, aligns closely with final grading decisions and demonstrates stronger clinical explanatory power for FHT. The index may offer a more robust and reproducible framework for embryo evaluation than the conventional Gardner system.

IMPACT STATEMENT:

The proposed grading system may reduce subjectivity, enhance expert agreement, and support the development of AI models trained on consensus-based rather than individual assessments.

원문 보기