*The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision DOI: http://dx.doi.org/10.5772/intechopen.110133*

use a model (CNN) to extract features from each frame, and then integrate all the extracted features with a recurrent model (e.g., long short-term memory network) following a timeline [2]. Focusing on applications, in those works that performed classification we found the study of breast lesions [88–90], thyroid nodules [88, 91], liver fibrosis [88, 92], and focal liver disease [88, 93]. Regarding the detection of lesions, some works focused on papillary thyroid carcinoma [94] and breast cancer [95]. Continuing in the detection task, but moving from lesions to the detection of the fetal standard plan, several papers proposed different methodologies [88, 96, 97]. These works constituted important pillars for the improvement of automatic guidance tools in the fetal US that could be embedded in image production software. Finally, in the segmentation task, several works have been registered with approaches in areas similar to those mentioned above, such as breast lesions [88, 98] and lymph node contouring [88, 99, 100]. However, in this part, there is also an application that has several works and that has an important diagnostic value in the clinical setting. This application is the detection of atheroma plaques in the carotid artery and the automation of this process would allow screening and prevention in a faster and more costeffective way [101, 102]. In fact, a multicenter clinical study has already been published to evaluate the feasibility of the technique [102].
