Stand-alone artificial intelligence - The future of breast cancer screening?

• Published in: Breast (Edinburgh) Vol. 49; pp. 254 - 260
• Main Authors: Sechopoulos, Ioannis, Mann, Ritse M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.02.2020; Elsevier
• Subjects: Artificial Intelligence; Breast - diagnostic imaging; Breast Neoplasms - diagnostic imaging; Deep learning; Early Detection of Cancer - methods; Early Detection of Cancer - trends; Female; Forecasting; Humans; Image Interpretation, Computer-Assisted - methods; Mammography; Mammography - methods; Mammography - trends; Screening; Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi; Deep learning; Screening; Mammography; Artificial intelligence
• ISSN: 0960-9776; 1532-3080; 1532-3080
• DOI: 10.1016/j.breast.2019.12.014

Although computers have had a role in interpretation of mammograms for at least two decades, their impact on performance has not lived up to expectations. However, in the last five years, the field of medical image analysis has undergone a revolution due to the introduction of deep learning convolutional neural networks – a form of artificial intelligence (AI). Because of their considerably higher performance compared to conventional computer aided detection methods, these AI algorithms have resulted in renewed interest in their potential for interpreting breast images in stand-alone mode. For this, first the actual capability of the algorithms, compared to breast radiologists, needs to be well understood. Although early studies have pointed to the comparable performance between AI systems and breast radiologists in interpreting mammograms, these comparisons have been performed in laboratory conditions with limited, enriched datasets. AI algorithms with performance comparable to breast radiologists could be used in a number of different ways, the most impactful being pre-selection, or triaging, of normal screening mammograms that would not need human interpretation. Initial studies evaluating this proposed use have shown very promising results, with the resulting accuracy of the complete screening process not being affected, but with a significant reduction in workload. There is a need to perform additional studies, especially prospective ones, with large screening data sets, to both gauge the actual stand-alone performance of these new algorithms, and the impact of the different implementation possibilities on screening programs. •AI-based mammography interpretation systems are feasible for stand-alone mode use.•Studies to date have shown that their performance approximates that of radiologists.•Larger scale, prospective screening trials are needed to determine their impact.•Once proven, AI identification of normal cases could reduce the radiologist workload.