# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 1 Introduction
### Computer-Assisted Ultrasound Scanning  
As indicated in Section 1.1, ultrasound imaging is highly operator-dependent. Therefore, sonographers must be properly trained to be able to acquire clinically useful images. Their training process has a steep learning curve, which is mainly caused by these factors:  
Figure 2: Simplified diagram of the diagnostic workflow with computer-assisted ultrasound devices.  
* The operator must place the transducer on the patient's body surface at precise locations to allow the signal to reach the desired organ and bounce back to the sensor. Bones (such as the ribs) and certain organs (such as the lungs) are opaque to pressure waves, so they must be avoided to reach the target organ.
* Both the exact location of the acoustic windows and the force to be exerted with the probe slightly depend on the patient's morphology.
* In conventional US imaging, the operator manually moves the transducer, obtaining a series of two-dimensional (2D) images of the target region. Then, he/she performs a mental 3D reconstruction of the underlying anatomy based on the content and the movement patterns used. Physicians have defined a set of protocols to obtain standard views that contain most of the relevant features in the 2D cutting plane of the organ. However, such views are not easy to locate for a person with limited training.
* Ultrasound images are noisy and are affected by signal dropout, attenuation, speckle, and acoustic shadows. So, the operator does not always perceive the anatomy as clearly as one would like. Moreover, a soft-textured image free of noise due to excessive filtering or incorrect gain settings is useless from a clinical point of view.  
Because of these unique challenges, computer-assisted scanning is a promising set of technologies that may democratize ultrasound medical imaging. Medical practitioners will benefit from any improvements in the effectiveness and autonomy of the acquisition workflow of this imaging modality. The smart guidance for assisted scanning will simplify their work, increase their productivity, decrease their expenses, and optimize their workflow.  
Its impact on developing countries can be even bigger. Because there are not enough skilled operators in developing countries, many women do not get any US exams during their pregnancy [12]. Creating a system that can help reduce the level of expertise needed during scanning could have a very significant impact. With this kind of system, any person with a basic anatomical background in a remote area would be able to do an ultrasound exam. Only images with clinical value would be sent to radiologist experts to be evaluated, no matter where the physician lives.  
We distinguish two main subfields in computer-assisted ultrasound scanning: view recognition and scan guidance, which are described in the following subsections. Tables 1-4 summarize the references analyzed in this work with information on the explored organ, the ultrasound scanner used, the number of cases, the type of AI model used, and some performance metrics. In any case, it is important to note that the variety of cases, US probes, and the significant lack of standardization in US imaging make it hard to compare the results from different publications.