Earticle

초록

영어

Background: Evaluating embryo quality is crucial for the success of in vitro fertilization procedures. Traditional methods, such as the Gardner grading system, rely on subjective human assessment of morphological features, leading to potential inconsistencies and errors. Artificial intelligence-powered grading systems offer a more objective and consistent approach by reducing human biases and enhancing accuracy and reliability. Methods: We evaluated the performance of five convolutional neural network architectures—EfficientNet-B0, InceptionV3, ResNet18, ResNet50, and VGG16— in grading blastocysts into five quality classes using only embryo images, without incorporating clinical or patient data. Transfer learning was applied to adapt pretrained models to our dataset, and data augmentation techniques were employed to improve model generalizability and address class imbalance. Results: EfficientNet-B0 outperformed the other architectures, achieving the highest accuracy, area under the receiver operating characteristic curve, and F1-score across all evaluation metrics. Gradient-weighted Class Activation Mapping was used to interpret the models’ decision-making processes, revealing that the most successful models predominantly focused on the inner cell mass, a critical determinant of embryo quality. Conclusions: Convolutional neural networks, particularly EfficientNet-B0, can significantly enhance the reliability and consistency of embryo grading in in vitro fertilization procedures by providing objective assessments based solely on embryo images. This approach offers a promising alternative to traditional subjective morphological evaluations.

목차

ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
Dataset preparation
Model selection
Data preprocessing and augmentation
Training procedure
Evaluation metrics
Grad-CAM visualization
RESULTS
Model comparison and performance overview
Model training and performance evaluation
ROC curve-based evaluation of classification models
Error analysis using confusion matrices
Interpretation of grad-CAM heatmaps and model performance
DISCUSSION
CONCLUSION
REFERENCES

키워드

저자

• Vincent Jaehyun Shim [ Cellular Reprogramming and Embryo Biotechnology Laboratory, Dental Research Institute, Seoul National University School of Dentistry, Seoul 08826, Korea ]
• Hosup Shim [ Department of Nanobiomedical Science, Dankook University, Cheonan 31116, Korea ]
• Sangho Roh [ Cellular Reprogramming and Embryo Biotechnology Laboratory, Dental Research Institute, Seoul National University School of Dentistry, Seoul 08826, Korea ] Corresponding Author

참고문헌

발행기관

간행물

관심저널 등록

• 간행물명

  Journal of Animal Reproduction and Biotechnology

• 간기

  계간

• pISSN

  2671-4639

• eISSN

  2671-4663

• 수록기간

  2019~2025

• 십진분류

  KDC 527 DDC 636