Stand-alone artificial intelligence - The future of breast cancer screening?
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 convolut...
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doaj-f8bf4035f0764be8a15308a90efe117b2020-11-25T04:05:07ZengElsevierBreast1532-30802020-02-0149254260Stand-alone artificial intelligence - The future of breast cancer screening?Ioannis Sechopoulos0Ritse M. Mann1Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands; Corresponding author. P.O. Box 9101, Route 766, 6500 HB, Nijmegen, the Netherlands.Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, the Netherlands; Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, the NetherlandsAlthough 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.http://www.sciencedirect.com/science/article/pii/S0960977619312214Artificial intelligenceDeep learningMammographyScreening |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ioannis Sechopoulos Ritse M. Mann |
spellingShingle |
Ioannis Sechopoulos Ritse M. Mann Stand-alone artificial intelligence - The future of breast cancer screening? Breast Artificial intelligence Deep learning Mammography Screening |
author_facet |
Ioannis Sechopoulos Ritse M. Mann |
author_sort |
Ioannis Sechopoulos |
title |
Stand-alone artificial intelligence - The future of breast cancer screening? |
title_short |
Stand-alone artificial intelligence - The future of breast cancer screening? |
title_full |
Stand-alone artificial intelligence - The future of breast cancer screening? |
title_fullStr |
Stand-alone artificial intelligence - The future of breast cancer screening? |
title_full_unstemmed |
Stand-alone artificial intelligence - The future of breast cancer screening? |
title_sort |
stand-alone artificial intelligence - the future of breast cancer screening? |
publisher |
Elsevier |
series |
Breast |
issn |
1532-3080 |
publishDate |
2020-02-01 |
description |
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. |
topic |
Artificial intelligence Deep learning Mammography Screening |
url |
http://www.sciencedirect.com/science/article/pii/S0960977619312214 |
work_keys_str_mv |
AT ioannissechopoulos standaloneartificialintelligencethefutureofbreastcancerscreening AT ritsemmann standaloneartificialintelligencethefutureofbreastcancerscreening |
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