# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 2 Standard Planes Recognition with AI
### Scan Plane Detection
[27] applied it for the automatic recognition of fetal facial standard planes (FFSPs). The results obtained with \"traditional\" methods before the deep-learning revolution were generally limited due to the following aspects:  
* Manual feature extraction settings depend on subjective human choices. * The number and type of extracted features are usually very limited. * Feature extraction methods cannot be optimized in real-time, which may be helpful when there are changes in the datasets. [PERSON] et al. [17] were one of the first research teams to exploit deep learning in ultrasound imaging. They employed a CNN with five convolutional and two fully connected layers to detect the standard abdominal plane. In this method, each input video frame is analyzed multiple times with overlapping image patches, which improves the accuracy but increases the computational cost. This work was extended to three scan planes and a recurrent architecture that considers temporal information, although it did not reach real-time performance [18]. A very challenging problem in standard plane recognition is the detection of the six standard planes in the fetal brain, which is required for fetal brain abnormality detection. The large diversity of fetal postures, similarities between the standard planes, and reduced amount of data cause these difficulties. [PERSON] et al. [28] approached this problem using a domain transfer learning approach based on a deep CNN and data augmentation, outperforming other deep learning methods. The automatic recognition of the standard abdominal circumference (AC) planes on fetal ultrasonography was tackled by [PERSON] et al. [29]. They developed SonoEyeNet, a CNN that used the information from experienced sonographers' eye movements. The collected eye movement data were used to generate heat maps with the visual fixation information of each frame, and then that data was used for plane identification. [PERSON] et al. [30] proposed a fast system for automatically detecting twelve fetal standard planes from real clinical fetal US scans, allowing real-time inference. They employed a CNN architecture inspired by AlexNet and freehand data (instead of data acquired in single sweeps), which provided much larger datasets. One of the best features of that work is a novel method to obtain localized saliency maps. This is useful, as localized annotations are not routinely recorded, and creating them for large datasets would require too much time. The saliency maps were applied to both guide the user into the neighboring volume of the desired view and build an approximate bounding box of the fetal anatomy. [PERSON] et al. extended this work with more data and a deeper neural network architecture [31] and transferred learning from natural to US images to speed up the training process. Recent works, such as [32], have also proposed this pretraining concept in several contexts where not enough ultrasound images are available. In general, supervised deep learning methods for ultrasound plane detection face the same problem: manual annotation of US videos is time-consuming and requires clinical expertise. Some recent works have explored new strategies to reduce the need for labeleddata and associated annotations significantly. [PERSON] et al. [33] proposed a semi-supervised learning framework for fetal head images. They formulated the learning as a multi-way classification problem, and the trained model can be used to label unseen video frames densely and automatically.