## 5 Blastocyst Grading Using Deep Learning-Based Methods
### Challenges in Deep Learning Approaches
Although a 3D camera is expected to improve the quality of the embryo assessment [51] and would be well suited to perform such an analysis, the modification and application of the proposed models on actual 3D imagery is expected to be more complicated and consume training times. In addition, 3D embryo images that were captured as 2D images by the TL machine with only one focal depth made it difficult for the AI model to track the distorted or overlapped cells and then make a correct recognition or classification [28]. Thus, it is important to note that the selection of correct data-driven inputs for machine learning models, particularly the deep learning, could give effect to model performance as well as the precision of grading the blastocyst quality. Moreover, the clinical characteristics (e.g., parents' age and infertility reason) of data should also be taken into account in developing the automated models. The influences of these factors on embryo development should not be ignored since a more accurate prediction model that incorporates clinical characteristics with video analysis should be established [40]. As an example, a study by [PERSON] et al. [73] explored the possibility of directly predicting the likelihood of pregnancy based on only embryo images that are labeled as \"positive live birth\" or \"negative live birth\". However, the result showed that the trained algorithm cannot identify positive live birth and negative live birth successfully using embryo morphology alone. This indicates that biased selection from sample collection could result in poor performance of ML models in a clinical setting [102]. The greatest challenge of implementing of AI models in an IVF grading system is currently on its stands, which does not incorporate additional information from different days of embryo development [83]. As proposed by [PERSON] et al. [103], characterizing the AI model for embryo assessment could improve the prediction of clinical pregnancy in terms of image quality, bias, and granularity of scores. However, this approach is only applicable on static IVF images at blastocyst stage. In fact, morphology and morphokinetics of blastocyst images has moderate agreement among embryologists on blastocyst implantation. A combination of deep learning models with computational algorithms can yield a continuous score that represents the quality of the embryo. As an example, the network that was trained to classify embryos based on their quality has performed well even in differentiating between embryos of the same class when combined with a genetic algorithm (GA) [88]. In addition, several multiple independent studies [78; 104] that focused on deep learning-based applications in embryo scoring have been suggested to improve the objectivity and consistency the of model but are limited in terms of generalization of the model. This is because if the networks model is overfit (i.e., memorize) during training, the model tends to perform poorly in terms of consistency. Furthermore, most neural networks classifier in deep learning models do not adapt well to different imaging systems and are limited to systems that were used in gathering the training data. Although CNN-based approaches provide an alternative to current methods of embryo scoring, limitations to their performance, among other factors, are dependent on the dataset used in training and the methodology involved in training such systems. In addition, the application of RNNs and GANs is still limited and further opening research is required in employing transfer learning systems, especially on un-supervised medical datasets.