Montjean, Debbie; Massoua, A Bandiang; Limandal, C; Lemacon, A; Huang, J Y; Diallo, Amirou; Benkhalifa, Moncef et Miron, Pierre (2025). Predictive model of good quality blastocyst development based on static image of fresh mature oocytes In: 41st Annual Meeting of the European Society of Human Reproduction and Embryology, 29 June-2 July 2025, Paris, France.
Ce document n'est pas hébergé sur EspaceINRS.Résumé
Study question: Can good quality blastocyst development be predicted
from a static images of fresh mature oocytes?
Summary answer: At deep learning model can be used to predict the blas-
tocyst outcome with the 0.694 confidence based on a static image of a
mature oocyte.
What is known already: The application of deep learning in in vitro fertiliza-
tion(IVF) laboratories has been a rapidly evolving area of research, aimed at
improving the efficiency, accuracy, and outcomes of IVF treatments. One of the
most significant applications of deep learning in IVF laboratories is the grading
and selection of embryos for implantation. But recently, oocyte is gaining more
interest in the context of fertility preservation and oocyte donation. The devel-
opment of reliable models to predict blastocyst development from a single
image of metaphase II oocyte may improve the counselling and management of
fertility preservation cycles as well as oocyte donors and recipients.
Study design, size, duration: This study aimed to develop a model for
good-quality blastocyst development prediction using non-invasive imaging of
fresh metaphase II oocytes. A dataset of 747 oocyte images with an imbal-
anced class distribution (70%blastocyst, 30%non-blastocyst) was used. Images
collected from one clinic required patient-wise data separation to ensure
meaningful evaluation. Preprocessing included grayscale conversion, normali-
zation to [0,1], resizing to 224 × 224pixels, and cross-validation with a
stratified group k-fold split to maintain class balance and patient separation
across folds.
Participants/materials, setting, methods: The designed machine learn-
ing model is based on a modified pre-trained VGG16-architecture to benefit
from transfer learning. The model was trained to distinguish oocytes likely to
develop into blastocysts. The imbalance class distribution was addressed us-
ing Focal Loss, enabling the model to prioritize harder-to-classify images
while balancing minority class gradients. The training involved two phases:
classifier-only training (three epochs) and end-to-end fine-tuning (ten epochs).
Augmentation techniques like image rotations, zoom, and intensity adjust-
ments enhanced robustness.
Main results and the role of chance: The preprocessing pipeline included
grayscale conversion, normalization, and cross-validation with a stratified
group k-fold split to ensure robust evaluation and prevent data leakage.
Given the dataset’s small size, a data-centric approach was adopted, focusing
on collecting high-quality oocyte images and cleaning to remove noise and
artifacts, maximizing data utility and clinical relevance. The use of Focal Loss
further addressed class imbalance, balancing sensitivity and specificity while
prioritizing harder-to-classify cases.
Building on these mentioned methods, the model demonstrated strong
predictive performance. Indeed, the model achieved an AUC-ROC of 0.694,
demonstrating good performance in predicting good quality blastocyst devel-
opment (Grade A and B based on Gardner grading system). Sensitivity and
specificity were balanced at 0.65 and 0.672, respectively, reflecting the mod-
el’s ability to handle the class imbalance. The negative predictive value (NPV)
(non-blastocyst development) was 0.382, while the positive predictive value
(PPV) (blastocyst development) reached 0.86, indicating superior perfor-
mance in identifying good quality blastocyst development. These results were
validated on an independent test set including 224 images with patient-wise
separation, ensuring clinical relevance and mitigating potential data leakage.
The balanced performance across sensitivity and specificity metrics supports
the model’s potential as a non-invasive predicting support tool for embryolo-
gists and clinicians.
Limitations, reasons for caution: The dataset included oocytes that de-
veloped into blastocysts and oocytes that did not reach the blastocyst stage,
excluding other developmental outcomes. Data from a single clinic limits gen-
eralizability. Additionally, the relatively low PPV underscores the need for
larger, multi-clinic datasets to validate the model’s robustness and clinical
applicability.
Wider implications of the findings: This study highlights the potential of
AI in non-invasive oocyte quality evaluation, supporting professionals in
counselling fertility preservation and IVF patients. By predicting good quality
blastocyst development, the model is expected to reduce subjective assess-
ment and improve the prediction of success rates. Expanding dataset will
enhance clinical impact and generalizability.
Trial registration number: No
| Type de document: | Document issu d'une conférence ou d'un atelier |
|---|---|
| Informations complémentaires: | Human Rproduction (2025) 40(suppl. 1):i250, deaf097.421 Affiche scientifique P-112 |
| Mots-clés libres: | - |
| Centre: | Centre INRS-Institut Armand Frappier |
| Date de dépôt: | 07 juill. 2025 19:05 |
| Dernière modification: | 07 juill. 2025 19:05 |
| URI: | https://espace.inrs.ca/id/eprint/16561 |
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