## 5 Blastocyst Grading Using Deep Learning-Based Methods
### Challenges in Deep Learning Approaches  
The main challenge in deep learning approaches to achieve better accuracy is about requirement of large dataset for training the automated grading model. Although there were many datasets available for medical image analysis [98], a very limited datasets are available in ART. The only available public dataset of human embryology specifically in blastocyst stage was provided by [67] and has been used in many studies as ground truth (GT). Other publicly available dataset of human embryology (day 3-5) is accessible at [[https://www.kaggle.com/ditzinsins/grades-embryo/discussion](https://www.kaggle.com/ditzinsins/grades-embryo/discussion)]([https://www.kaggle.com/ditzinsins/grades-embryo/discussion](https://www.kaggle.com/ditzinsins/grades-embryo/discussion)) (accessed on 1 August 2022), but is limited in the number of images and by its unknown source. Other than that, datasets of mouse embryos are publicly available for machine learning training provided by [100] and [[https://bbbc.broadinstitute.org/BBBC003](https://bbbc.broadinstitute.org/BBBC003)]([https://bbbc.broadinstitute.org/BBBC003](https://bbbc.broadinstitute.org/BBBC003)) (accessed on 2 August 2022) but may not suit the detection model for human embryos due to their different physical appearance. So far, the only study that used deep learning with a large dataset of microscopic embryo images was conducted by [101] to develop a CNN-based prediction model. With more than 170,000 embryo raw images of the Asian population and three classification categories of blastocyst (ICM, and TE), the study obtained promising results of 75.36% predictive accuracy. Vision processing results using deep learning are also dependent on image resolution. Achieving adequate performance in object classification, for example, requires high-resolution images or video--with the consequent increase in the amount of data that needs to be processed, stored, and transferred. Image resolution is especially important for applications in which it is necessary to detect and classify objects in the distance, e.g., in security camera footage. The frame reduction techniques discussed previously such as using SIFT features [22; 36] or optical flow for moving objects [23] to first identify a region of interest are useful with respect to image resolution and also with respect to reducing the time and data required for training. Figure 7: Score distribution of various deep learning models employed in blastocyst study. Among all reviewed papers, the study that most focused on blastocyst development was conducted by [54] to analyze the expanding, hatching, or hatched embryos and its related morphological features. However, it is important to consider the morpho-kinetic parameters and morphological features since these two are complementary markers for embryo selection and blastocyst prediction. In addition, the integration of spatial and temporal information is crucial to accurately predict the blastocyst formation for potential embryo growth [28]. As proven by many studies, the use of temporal information from time-lapse imaging can indeed improve model performance [18; 32; 91; 94] as achieved by [94], in which the accuracy improvements have been increased to 7.2% and 5.1% for ICM and TE grading, respectively. A data-driven nature plays a crucial role in terms of the implementation of deep leaning model applications in an automated, precise, and robust method for application to explore and delineate specific features of an embryo development at blastocyst. In many applications, all images used to train the system were static, however in the real scenario, the embryo development is a dynamic process. Therefore, the static images cannot reflect the entire status of an embryo and may affected the learning algorithms in machine learning models [74]. In addition, the image inputs for prediction models may vary in terms of 2D or 3D grayscale images obtained either from the image captured or time-lapse videos.