# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 3 Scanning Guidance with AI  
US is used more frequently in clinical practice than other medical modalities such as magnetic resonance imaging (MRI) or computed tomography (CT). Furthermore, it is carried out by a wider variety of medical professionals with different levels of expertise [56]. However, maneuvering a US transducer requires hand-eye coordination that takes years to master [12]. The US examiner has to guide the transducer to specific scan planes through a highly variable anatomy and, at the same time, interpret the findings and make decisions.  
Scanning guidance goes beyond the tasks of standard plane identification. The potential for utilizing AI strategies in ultrasound scanning guidance to improve the clinical workflow is high, as such features could increase diagnostic confidence and productivity  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{3}{c}{**Standard Clinical Plane Extraction from 3D Volume**} & \\
**Reference** & **Target Organ** & **\# Cases** & **US System** & **AI Model** & **Results** \\ \hline [PERSON] et al. [37] & Heart & 25 3D cases & Philips iE33 & RF & Distance \textless{6 mm}, Angle \textless{3.5}\textless{}^{\circ}\) \\ [PERSON] et al. [38] & Fetal Abdomen & 126 training & Several & RF & Plane offset Error 5.8 mm \\ [PERSON] et al. [39; 40] & Fetal Brain & 72 volumes & - & CNN (ITN) & Translation error 3.5 mm \\ [PERSON] et al. [41] & Fetal Brain & 19,838 images & Several & & 96.9\% Dice coefficient \\  & & (10,595 scans) & & & (Head Region Detection) \\ [PERSON] et al. [42] & Fetal Brain & 430 & & Mindray DC-\# US & RNN recurrent neural network \\ [PERSON] et al. [43] & Ulerus & 683 (Ulerus) & - & RL, RNN, & Localization Accuracy \\  & and Fetal Brain & 432 (Field Brain) & - & and DQN & Fetal Brain 1.19 mm \\ [PERSON] et al. [46] & Phantom & 6 volumes & Ultrasonix & RL, RNN, & Translation error 5.5 mm \\  & & & SonixTouch & and DQN & Angular error 2.2 deg \\ T.-[PERSON] et al. [47] & Breast & & 22,982,656 images (1 & CE ABUS & CNN & (0.14 mm/0.25 mm) \\  & & volume) & Philips Healthcare & & Translation error 3.4 mm \\ [PERSON] et al. [48] & Fetal Brain & 551 images & EpiQ & & CNN (ITN) & Angular error 12.5 deg \\ [PERSON] et al. [49] & Prostate & 108 image pairs & Intraoperative TRUS & PointNet NN & Registration Error 4.4 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of publications on standard clinical plane extraction from volumes.  
as well as shorten the learning curve for new users. Many early studies indicate that integrating AI into this task could improve the performance of a less-experienced radiologist to nearly the level of an expert. Besides, this kind of development might reduce scan times in traditionally lengthy exams so that it can improve the scan according to ALARA considerations. For inexperienced users, information from real-time tools might provide support on how to position the transducer correctly and how to reach the target organ.  
Most published solutions for scanning guidance with AI are still in the early stages of development. It is a challenging task due to the images' complexity and the large variability in patients' anatomy and operators. Here we cover the most prominent research advances in the last few years and the open challenges and opportunities in this field.