2019
DOI: 10.4329/wjr.v11.i2.19
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Artificial intelligence in breast ultrasound

Abstract: Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of… Show more

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Cited by 90 publications
(51 citation statements)
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“…Another major drawback is identification of lesion boundaries leading to interoperator variability. Research on various machine learning, artificial intelligence and 3D reconstruction algorithms that can enhance the detection of tumor boundaries and reduce operator variability is currently being pursued which will further the utility of USI in PDT treatment monitoring .…”
Section: Discussionmentioning
confidence: 99%
“…Another major drawback is identification of lesion boundaries leading to interoperator variability. Research on various machine learning, artificial intelligence and 3D reconstruction algorithms that can enhance the detection of tumor boundaries and reduce operator variability is currently being pursued which will further the utility of USI in PDT treatment monitoring .…”
Section: Discussionmentioning
confidence: 99%
“…In broad terms, we highlight two AI techniques with applications within the imaging field—machine learning (ML)-based algorithms with its more advanced class of DL. Convolutional neural networks (CNNs) are the most popular DL architecture used in medical imaging, although they require large amounts of training data [ 123 , 124 ]. AI-based algorithms that guide the examiner towards the best image acquiring position have been developed, so that, in the future, the examiner will not necessarily need to have previous US technique knowledge.…”
Section: Artificial Intelligence In the Ultrasonographic Evaluatiomentioning
confidence: 99%
“…Inflammatory reactions in the liver can be displayed by perihepatic lymphadenopathy [28][29][30][31][32][33][34][35][36][37], focal changes to vascularity and perfusion in the surrounding liver parenchyma [38][39][40], or by demonstrating increased metabolic turnover using PET technology. The role of artificial intelligence in the imaging of CE is promising but has not been evaluated so far [41,42].…”
Section: Differentiating Vital and Avital Echinococcosismentioning
confidence: 99%