# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 2 Standard Planes Recognition with AI
### Scan Plane Detection
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Reference**} & \multicolumn{2}{c}{**Scan Plane Detection, Plane Classification, and Organ Identification**} & \multicolumn{1}{c}{**US System**} & \multicolumn{1}{c}{**AI Model**} & \multicolumn{1}{c}{**Results**} \\ \hline R.-[PERSON] et al. [14] & Breast & 250 images & ATL HDI 3000 & SVM & Accuracy 95.4\% \\ [PERSON] et al. [15] & Heart & 91 videos from 12 subjects & GE Voluson E8 & RF & Classification error \\ [PERSON] et al. [16] & Kidney & 66 images & Several (Databases) & Multilayer ANN & Accuracy 97\%. \\ [PERSON] et al. [17] & Harin, Stomach, & 300 training & Siemens Acuson & RNN & AUC 0.94 \\ [PERSON] et al. [18] & Spine, Heart & 331 evaluation & Sequoia 512 US & CNN & Accuracy 90.4\% \\ [PERSON] et al. [18] & Fetal Abdomen & 20,660 images & Unknown & CNN & Accuracy 90.4\% \\ [PERSON] et al. [19] & Fetal Abdomen & 1664 images & Siemens Acuson & AdaBoost, Selective \\  & 100 videos & Sequoia 512 & Search (SSD/SSSD) & Accuracy 81\% (\%\)(SSSD) \\ [PERSON] et al. [20] & Fetal Abdomen & 1995 images & Siemens Acuson & RF + SVM & Accuracy 85.6\% \\ [PERSON] & Fetal Abdomen & 284 images & Philips HD9 & AdaBoost & Accuracy 80.7\% \\ [PERSON] & Fetal Abdomen & 8749 images & Philips HD9 & Local Phase Feature & AUC 0.88 \\ [PERSON] et al. [23] & Heart & 270 videos & Philips CC30 & SVM & Accuracy 98.5\% \\ [PERSON] et al. [24] & Heart & 703 images & GE Vivid series & SVM & Accuracy 98\% \\ [PERSON] et al. [25] & Heart & 113 videos & Several (Database) & SVM & Recognition rate 81\% \\ [PERSON] et al. [26] & Liver & 20 livers & frame grabber & SVM & Accuracy 91\% \\ [PERSON] et al. [27] & Fetal Brain & 1,735 images & Siemens Acuson & SVM Classifier & Accuracy 93.2\%. \\ [PERSON] et al. [28] & Fetal Brain & 31,200 images & Hitachi ARETTA 70 & CNN & AUC 0.90 \\ [PERSON] et al. [29] & Fetal Abdomen & 1292 images & Phillips HD9 (V7-3) & CNN & Precision 96.5\% \\ [PERSON] et al. [30] & Fetal Anatomy & 1003 videos & GE Voluson E8 & CNN & Accuracy 66-90\% \\ [PERSON] et al. [51] & Fetal Anatomy & 2694 videos & GE Voluson E8 & CNN & Accuracy 90.1\% \\ [PERSON] et al. [32] & Lung and Liver & 23,000 images & Several (Database) & CNN & Accuracy 94.2\% \\ [PERSON] et al. [33] & Fetal head & \(>\)30,000 frames & Kontel GEN 1 C10R & CNN & Precision 86\% \\ [PERSON] et al. [34] & Fetal brain & 19,838 images (10,595 & Several & Agreement 94.1\% \\ [PERSON] et al. [35] & Fetus and Mother’s & 12,400 images (1792 & Several (Database) & CNN (19 models) & Accuracy \\ [PERSON] et al. [15] & Fetal Heart & 91 videos & GE Voluson E8 & RF & 18\% \\ [PERSON] et al. [36] & Fetal Heart & 63 videos & - & Fourier and SVM & Accuracy 88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the selected publications on scanning, plane detection, plane classification, and organ identification. AdaBoost, similar to other early works based on Haar-like features extracted from the data and AdaBoost or Random Forest classifiers [22; 23]. [PERSON] et al. [21; 22] proposed detecting anatomical structures from abdominal US images by the integration of their global and local features. However, the method was semi-automatic, and the detection accuracy was not good enough for clinical use. Other pre-deep-learning works were based on object classification methods. An 8-way echocardiogram view classification was obtained in [23] with a Gabor filter and a kernel-based SVM classifier. The work in [24] utilized the Histograms of Oriented Gradients (HOG) feature and SVM classifier. In [25], 2D echocardiogram view classification was obtained based on a set of novel salient features obtained from local spatial, textural, and kinetic information. An SVM classifier with a pyramid-matching kernel was used to learn a hierarchical feature dictionary for view classification. [PERSON] et al. [26] also used an SVM classifier for the features extracted using wavelet decomposition and a gray-level co-occurrence matrix, and [PERSON] et al.