## 4 Morphology of the Blastocyst and Quality Assessment
### Blastocyst Grading Approaches in IVF
#### 4.2.4 Machine Learning-Based Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Approach** & **Description** \\ \hline Embryo scoring based on classification tree model [47] & \(\bullet\) & Subdivided embryos into 4 categories (A, B, C, D). \\ \hline \multirow{4}{*}{\begin{tabular}{c} Addition of PN scoring [15] \\ \end{tabular} } & \(\bullet\) & Additional criterion to improve prediction of embryo implantation potential. \\  & \(\bullet\) & Evaluated by chi-square test or Kruskal–Wallis test. \\  & \(\bullet\) & Implantation success 95\%. \\ \hline \multirow{2}{*}{ASEBIR [10]} & \(\bullet\) & Scoring system for early embryos, morulae, and blastocysts. \\  & \(\bullet\) & Exclusively based on morphological parameters. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Semi-Automated \\ \end{tabular} } & \(\bullet\) & Using image algorithm of ZP, TE and ICM \\  & \(\bullet\) & Classifier-statistical analysis. \\  & \(\bullet\) & Accuracy 67\%. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Semi-Automated \\ \end{tabular} } & \(\bullet\) & Measure blastocyst internal area, ICM area, and TE cells number. \\  & \(\bullet\) & Statistical analysis—Sigmaplot. \\ \hline \multirow{2}{*}{\begin{tabular}{c} Semi-Automated \\ \end{tabular} } & Embryo growth classification [58] & \(\bullet\) & Image intensity variance + circular Hough Transform. \\  & \(\bullet\) & Embryo compaction and cavitation stage. \\ \hline \multirow{2}{*}{\begin{tabular}{c} Embryo vitrification [43] \\ \end{tabular} } & \(\bullet\) & Automation instrument (Gavi system). \\ \hline \multirow{2}{*}{\begin{tabular}{c} Embryo development \\ stages [84] \\ \end{tabular} } & \(\bullet\) & Data extraction using localize variance based on image distribution. \\ \hline \multirow{2}{*}{\begin{tabular}{c} Vitrification \\ procedure [85] \\ \end{tabular} } & \(\bullet\) & Using GAVITM system. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Prediction of human \\ embryo fitness [58] \\ \end{tabular} } & \(\bullet\) & Image processing and Segmentation of TE, blastocyst, and ICM. \\  & \(\bullet\) & Accuracy 95–96\%. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Bovine blastocyst quality \\ classification [80] \\ \end{tabular} } & \(\bullet\) & Combined GA and ANN. \\  & \(\bullet\) & Classifying embryo quality grades (3 classes). \\  & \(\bullet\) & Accuracy 76.4\%. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Fully Automated \\ \end{tabular} } & \(\bullet\) & DCT transform and ANN. \\  & \(\bullet\) & Precision 80\% (ZP), 69\% (TE) \& 76\% (ICM). \\ \hline \multirow{4}{*}{\begin{tabular}{c} Fully Automated \\ \end{tabular} } & \(\bullet\) & Level Set segmentation algorithm. \\  & \(\bullet\) & Accuracy 87.8\%. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Localization of cleaving \\ embryo (Day 2) [80] \\ \end{tabular} } & \(\bullet\) & Unsupervised segmentation. \\  & \(\bullet\) & Accuracy 96\%. \\ \hline \multirow{2}{*}{\begin{tabular}{c} Grading quality of \\ bovine blastocyst [79] \\ \end{tabular} } & \(\bullet\) & Texture and geometrical features extraction. \\  & \(\bullet\) & ANN + GA. \\  & \(\bullet\) & 77.8\% (smartphone) and 85.7\% (Blasto3Q). \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Bovine quantitative \\ variable determination \\ using image \\ processing [52] \\ \end{tabular} } & \(\bullet\) & Extraction of embryonic morphological information. \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Human blastocyst \\ segmentation [67] \\ \end{tabular} } & \(\bullet\) & Segmentation and texture analysis. \\  & \(\bullet\) & Accuracy 86.6\% (TE) and 91.3\% (ICM). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._