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Purpose of Review: To review research on deep learning models and their potential application within breast screening.Recent Findings: The greatest issue in breast screening is a workforce crisis across the UK, much of Europe and even Japan. Traditional computer-aided detection (CAD) for mammography decision-support could not reach the level of an independent reader. Deep learning (DL) outperforms CAD and is close to surpassing human performance. DL is already capable of decision support and density assessment for 2D full-field digital mammography (FFDM), and is on the cusp of providing consistent, accurate and interpretable mammography reading as an independent reader.Summary: A bold vision for the future of breast cancer screening is required if programmes are to maintain double reading standards. DL provides the potential for single reading programmes, such as in the USA, to reach EU double reading accuracy, as well as providing practical support for adoption of the emerging modality of digital breast tomosynthesis. [ABSTRACT FROM AUTHOR]

In this article, we will review the current recommendations for screening breast ultrasound along with its role as an adjunct to mammography and as a primary screening tool. We will discuss the most recent literature pertaining to breast ultrasound screening in high-risk, intermediate-risk, and average-risk women and compare it with other breast screening modalities including breast MRI, tomosynthesis or 3D mammography, and automated breast ultrasound. The current obstacles to screening breast ultrasound's more widespread implementation will also be covered in our discussion. These will include ultrasound's high false positive rate when compared to mammography, overdiagnosis, and cost-effectiveness. Our target audience encompasses breast surgeons, oncologists, and breast radiologists with an interest in screening breast ultrasound. [ABSTRACT FROM AUTHOR]

Digital 2D mammography is the current standard of care for breast cancer screening. False positives and overdiagnosis have been cited as potential harms of screening mammography, and efforts to improve this technology have led to the development of digital breast tomosynthesis (DBT). The literature thus far has shown screening with tomosynthesis to simultaneously decrease recall rates and increase cancer detection. This review evaluates the possibility for 2D plus tomosynthesis mammography to replace conventional 2D screening. It outlines the requirements of an effective screening modality, reviews early and recent literature on performance outcomes, including results by age and density, and highlights potential challenges. [ABSTRACT FROM AUTHOR]