Abstract
In Japan, mammography is commonly used for breast cancer screening. However, the mortality rate has not decreased, possibly due to the low screening uptake and the high prevalence of dense breast tissue among Japanese women, which reduces mammography's effectiveness. A recent prospective study in Japan, J-START, demonstrated that combining mammography with ultrasonography increases detection rates and reduces the incidence of interval cancers, highlighting the significance of ultrasound examinations.
Artificial Intelligence (AI) technologies, particularly in machine learning and deep learning, offer promising solutions to enhance the accuracy and efficiency of breast ultrasound diagnostics. This review explores AI's current capabilities in breast ultrasound imaging, emphasizing key advancements in breast lesion detection and diagnosis. Additionally, we introduce AI-based breast ultrasound diagnostic support programs approved by the Pharmaceuticals and Medical Devices Agency, which include programs for detecting lesion candidate regions and diagnosing the necessity of further examination based on detected lesion candidates. These AI tools are expected to improve diagnostic accuracy and efficiency.
While AI holds significant promise, several challenges remain. It is essential for physicians to oversee its use responsibly, as there are concerns regarding patient acceptance and environmental impact. This review underscores the revolutionary potential of AI in breast cancer diagnostics and emphasizes the importance of ongoing research and development to overcome existing limitations.
