## 5 Blastocyst Grading Using Deep Learning-Based Methods
### Implementation of Deep Learning Models  
One recent work implemented a deep learning-based system that was trained with a genetic algorithm (CNNg) and showed capability to differentiate between euploid blastocysts based on their capacity for implantation [88]. The research indicates that the artificial intelligence-based approaches have a high potential to improve success rates of IVF with a significantly better agreement with the actual implantation outcome for embryos with higher implantation scores. In addition, the less complex deep learning model such as Resnet was found feasible for grading cleavage embryos (at day 3) to achieve better performance accuracy [89]. However, this study implemented a relatively simpler dataset and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Approach** & **Description** \\ \hline Predict blastocyst & \(\bullet\) & Based on cytoplasm movement velocity \\ development [53] & \(\bullet\) & K-NN, LSTM-NN, hybrid ensemble classifier \\  & \(\bullet\) & Accuracy 82.6\% \\ \hline Improve blastocyst & \(\bullet\) & ANNs. \\ morphology [77] & \(\bullet\) & Mouse and bovine blastocyst. \\ \hline Predict implantation & \(\bullet\) & Random Forest model. \\ after blastocyst & \(\bullet\) & AUC 0.74. \\ transfer [75] & & \\ \hline Analysis embryo quality: & \(\bullet\) & 4D segmentation mouse embryo. \\ Preliminary study [64] & \(\bullet\) & HI value of healthy embryo. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 6: Example of deep learning models for ART (**a**) Generative model. CNN can be trained to generate images including tasks of image pre-processing, image enhancement, image augmentation and/or others. (**b**) Multimodal discriminative model. Deep learning architectures can be constructed to jointly learn from images (typically with CNN) and non-image data (typically with general deep networks). had limited resource constrained settings, i.e., an image capturing process that resulted in different shades of colors and obstruction. In more advanced models, the integration of deep learning models and other tools like Raman spectroscopy is applicable for predicting whether the cleavage stage embryo at day 3 has potential to develop into a blastocyst or not [89]. This combination in biological detection could help embryologists to provide a target research direction of RNA nuclei acid-related molecules for a follow-up biomarker study. Recent advances in IVF technology demands expensive equipment technologies such as intracytoplasmic sperm injection (ICSI), time-lapse imaging, and pre-implantation genetic screening to help fertility centers in improving clinical outcomes. Due to the need to set up laboratories and handle IVF processes, these technologies might not be available in the majority of underdeveloped nations, hence the suggestion to use portable and inexpensive optical systems employing smartphones, which gave adequate-quality images as a stand-alone imaging system [89]. Although the proposed work was potentially performed with high detection accuracy, the platform will require multiple imaging stations to provide morphological features as a TL imaging system. To highlight the effectiveness of the deep learning-based methods, some of the state-of-the-art technology in segmentation and classification architectures were deployed for semantic segmentation of human blastocyst images [49, 57, 62, 90]. These models were reimplemented, trained and tested on the various benchmark datasets and comparison results were summarized in Table 3. The current focus of DL applications in embryology can be categorized into the following groups: classification, prediction, segmentation, localization, and detection.