Application of deep learning on automated breast ultrasound: Current developments, challenges, and opportunities - Ruixin Wang; Zhiyuan Wang; Yuanming Xiao; Xiaohui Liu; Guoping Tan; Jun Liu (Meta-Radiology, 2025)
Breast cancer is a major disease threatening the health of women worldwide. The advent of automated breast ultrasound (ABUS) has provided new possibilities for the early screening and diagnosis of breast cancer. Concurrently, artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems, driven by deep learning (DL), have advanced significantly over the past decade. Unlike traditional handheld ultrasound (HHUS), ABUS enables the separation of scanning and diagnosis, increasing the demand for CAD systems that hold significant clinical value. In recent years, DL has become a dominant force in AI development, playing a crucial role in CAD for across various medical imaging modalities. However, despite its prominence in AI-driven medical image analysis, a comprehensive review of its applications in ABUS is still lacking. This paper provides a detailed analysis of the latest advancements, existing challenges, and future research opportunities in this rapidly evolving field.