Ultrasound and duplex sonography are complex, evolving technologies that allow the assessment of nearly all soft tissues and parenchymatous organs. Duplex sonography provides additional information on volumetric blood ow in numerous healthy and pathologic conditions. Ultrasound offers many advantages, including lack of radiation exposure, convenience, high diagnostic accuracy, dynamic procedure, and low cost, but ultrasound approaches are specialized techniques that require intensive training and experience by the examiner. Furthermore, results are often examiner- dependent, and interpretation some­times appears inconsistent. Methods to evaluate ultrasound ndings that are more reliable and objective probably can be facilitated by computerized analysis [41, 42]. Therefore much effort has been made to develop computer-aided diagnosis (CAD) systems for ultrasound examinations, using either the man-made ultrasound features or deep learning systems [43], consisting of software that is regarded as able to learn better and faster than a human examiner with no prior experience [44]. In the rst clinical trials, deep learning software systems were applied for classication of breast cancer and proved highly accurate (even with comparably few training cases), and their fast evaluation speed made their use feasible for real- time image analysis [44]. Rodríguez-Cristerna et al. conducted a study of conventional CAD systems for breast ultrasound. They combined CAD with a breast imaging/reporting and data system and found that their approach had an increased sensitiv­ity with unchanged high specicity, which signicantly improved the performance of CAD systems in the classica­tion of breast lesions [45]. Other authors described automated breast ultrasound techniques that produced a full 3D scan of the breast automatically, with lesion segmentation; this system reduced operator dependency and facilitated double reading and comparison with past exams [46].