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Breast cancer is the most commonly diagnosed cancer in women and its incidence and the mortality associated with it have increased over the years. Early detection of breast cancer via various imaging modalities can significantly improve the prognosis of patients. Ultrasound is a useful imaging tool for breast lesion characterization due to its acceptable diagnostic performance and non-invasive and real-time capabilities. However, one of the major drawbacks of ultrasound imaging is operator dependence. Artificial intelligence (AI), particularly deep learning, is gaining extensive attention for its excellent performance in image recognition. AI can make a quantitative assessment by recognizing imaging information, thereby improving ultrasound performance in the diagnosis of breast cancer lesions. The use of AI for breast ultrasound in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skill in some cases. This review article discusses the basic technical knowledge required, the algorithms of AI for breast ultrasound, and the application of AI in image identification, segmentation, extraction, and classification. In addition, we also discuss the future perspectives for the application of AI in breast ultrasound. |
