# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 1 Introduction
### Main Applications of AI in US Imaging  
Figure 2 shows a diagram of the diagnostic workflow with computer-assisted US devices, where the main tasks performed during the examination are outlined. Some of them are done by practitioners (and technicians), others are in charge of the hardware, but a significant and growing portion of them are evolving as advanced software pieces that try to improve the efficiency of the two previous actors. The illustrated workflow starts by placing the probe on the patient's body surface, which will be traversed by pressure waves whose echo is accurately measured. US imaging systems process the recorded signals to generate 2D images, which are taken to perform measurements and diagnoses. Assistance can be provided in the examination procedure by providing indications on how to perform the scan, recognizing the scanned organ, and making additional assessments about how to improve the image quality in the acquired frames.  
Beamforming, higher resolution, and image enhancement are three areas in which conventional signal processing can be improved. Computer assistance tools could also replace repetitive measurement and quantification tasks. Additionally, physicians will be able to consider additional feedback from computer-aided diagnosis and computer-aided triage tools.  
Figure 1: Exponential growth in the number of articles indexed in PubMed with “deep learning” and “ultrasound” (blue) and “deep learning” applied to other data and medical image modalities.  
There are four main research areas where artificial intelligence and computer-assisted solutions may help standardize medical practice, reduce training and examination times, and improve ultrasound image quality.  
1. The application of machine learning techniques to ultrasound image formation has a large margin of improvement to provide less blurry and more significant images, even by creating new ultrasound medical imaging modalities [11]. New approaches in beamforming, super-resolution, and image enhancement require modifications to hardware elements, which are usually more ambitious than more straightforward software post-processing approaches. In spite of the more difficult adoption, many research advances are overcoming traditional and simple reconstruction algorithms, which translate physical measurements of ultrasound waves into images in the display. The more computational power available in medical devices, the more sophisticated real-time solutions may enrich the different ultrasound imaging modalities.
2. AI algorithms can help to reduce the steep learning curve in ultrasound scanning by assisting physicians, nurses, and technicians to perform full examinations, as is shown in detail in the following sections.
3. Image processing algorithms for measurement, quantification, and computer-aided detection have left behind traditional feature engineering, and the most competitive solutions in ultrasound imaging take advantage of deep learning-based approaches.
4. Finally, computer-assisted diagnosis, triage, detection, and quantification have also attracted the research community's attention because of their significant impact on reducing physicians' workload.  
There are a large number of applications and publications in this field. Therefore, this state-of-the-art review focuses on the latest advances in computer-assisted ultrasound scanning. The search strategy followed sought to obtain a broad overall view of the state-of-the-art. Specific terms on Pubmed, the most common free database of publications on medical imaging, such as \"ultrasound\", \"AI\", \"computer-assisted\" and \"AI-guided,\" were used to obtain the references considered in this review.