## 4 Morphology of the Blastocyst and Quality Assessment
### Blastocyst Grading Approaches in IVF
#### 4.2.4 Machine Learning-Based Methods
\[\sigma=\sqrt{\frac{\sum\limits_{i}{\left({x_{i}-\overline{x}}\right)^{2}}}{ \left({N-1}\right)}} \tag{1}\]  
\[Entropy=-\sum\limits_{i}{p_{i}}.\log_{2}(p_{i}) \tag{2}\]  
In addition, the single detection of a blastocyst component such as ZP [59; 60], ICM [61; 62], or TE [63] is easier to segment as compared to the detection of all-combined blastocyst components in an image that may need a deep learning model in the network [70; 71]. As suggested by [30], the blastocyst grading for patients aged 35 years or above shall be performed using a strict grading policy in terms of TE, ICM and expansion grades together rather than using a single blastocyst parameter to choose the best combined-score blastocyst. The adaptations of image processing algorithms in machine learning-based models for segmenting and classifying the blastocyst components in fertilized embryo have influence and impacts in grading the embryo quality. As an example, semantic segmentation of images provides an important cornerstone for subsequent tasks of image analysis and understanding by showing better comprehension of multiscale structural features of different blastocyst components [48]. Other novel automatic-based area segmentation by using lightness value was proposed [72] to enhance the quality of input images by deemphasizing blastocyst cavity regions and those areas outside the embryo. The work employed Retinex algorithm to remove small gradients inside and outside the blastocyst region so that cavity region is smoothed. Meanwhile, the rapid progress in various image processing algorithms, and the associated developments in computer-aided systems classifier such as the decision tree [50; 73], CNN [74], random forest [75; 76], SVM [43; 52], Naive Bayes classifier [52], neural network [53; 69; 77; 78; 79; 80; 81] and deep learning has propelled IVF grading into one of the most important sub-fields in assisted reproduction for humans, and for animals as well. Moreover, the combination of image processing methods and deep learning techniques has seen the development of a robust model such as Life Whisperer AI [82], which has been used as a clinical decision support tool for the prediction of embryo viability during IVF procedures. However, there exist several limitations of different machine learning algorithms in automated analysis based on computer vision technology as reviewed by [8; 10; 83]. Several advantages have been highlighted in the review, particularly in fully-automated machine learning (Table 2); however, limitations in the form of requirement for a massive dataset for training and high-end computing power are the main issues of those areas. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Approach** & & **Description** \\ \hline \multirow{6}{*}{Conventional/manual} & Simplified blastocyst grading system [30] & \(\bullet\) & Inter and intra-observer (5 embryosologists). \\  & & \(\bullet\) & Three classes of \(A\), \(B\), \(C\), Cavitating and Compacting (based on \\  & & & blastocyst expansion, ICM and TE). \\  & & \(\bullet\) & Implantation rates 79.1\% (grade A) and 13.25 (cavitating). \\ \cline{2-4}  & \multirow{2}{*}{Morphological embryo selection [21]} & \(\bullet\) & Used [PERSON]’s grading. \\ \cline{1-1}  & & \(\bullet\) & Implemented chi-square test on embryo grades. \\ \cline{1-1}  & & \(\bullet\) & Implantation rate 55.9\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: This is a table. Tables should be placed in the main text near to the first time they are cited.