Artificial Intelligence: A Primer for Breast Imaging Radiologists

被引:33
作者
Bahl, Manisha [1 ]
机构
[1] Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02114 USA
基金
美国国家卫生研究院;
关键词
artificial intelligence; machine learning; deep learning; breast imaging; mammography; COMPUTER-AIDED DETECTION; HIGH-RISK; DIGITAL MAMMOGRAPHY; CANCER; AI; TOMOSYNTHESIS; PERFORMANCE; DIAGNOSIS; ACCURACY; ARTICLES;
D O I
10.1093/jbi/wbaa033
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimateYYly ensure optimal patient care.
引用
收藏
页码:304 / 314
页数:11
相关论文
共 84 条
[1]   Deep convolutional neural networks for mammography: advances, challenges and applications [J].
Abdelhafiz, Dina ;
Yang, Clifford ;
Ammar, Reda ;
Nabavi, Sheida .
BMC BIOINFORMATICS, 2019, 20 (Suppl 11)
[2]   A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow [J].
Akkus, Zeynettin ;
Cai, Jason ;
Boonrod, Arunnit ;
Zeinoddini, Atefeh ;
Weston, Alexander D. ;
Philbrick, Kenneth A. ;
Erickson, Bradley J. .
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2019, 16 (09) :1318-1328
[3]   Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms [J].
Akselrod-Ballin, Ayelet ;
Chorev, Michal ;
Shoshan, Yoel ;
Spiro, Adam ;
Hazan, Alon ;
Melamed, Roie ;
Barkan, Ella ;
Herzel, Esma ;
Naor, Shaked ;
Karavani, Ehud ;
Koren, Gideon ;
Goldscbmidt, Yaara ;
Shalev, Varda ;
Rosen-Zvi, Michal ;
Guindy, Michal .
RADIOLOGY, 2019, 292 (02) :331-342
[4]   Assessing Women at High Risk of Breast Cancer: A Review of Risk Assessment Models [J].
Amir, Eitan ;
Freedman, Orit C. ;
Seruga, Bostjan ;
Evans, D. Gareth .
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE, 2010, 102 (10) :680-691
[5]   A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification [J].
Arieno, Andrea ;
Chan, Ariane ;
Destounis, Stamatia V. .
AMERICAN JOURNAL OF ROENTGENOLOGY, 2019, 212 (02) :259-270
[6]   Harnessing the Power of Deep Learning to Assess Breast Cancer Risk [J].
Bahl, Manisha .
RADIOLOGY, 2020, 294 (02) :273-274
[7]   Detecting Breast Cancers with Mammography: Will AI Succeed Where Traditional CAD Failed? [J].
Bahl, Manisha .
RADIOLOGY, 2019, 290 (02) :315-316
[8]   High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision [J].
Bahl, Manisha ;
Barzilay, Regina ;
Yedidia, Adam B. ;
Locascio, Nicholas J. ;
Yu, Lili ;
Lehman, Constance D. .
RADIOLOGY, 2018, 286 (03) :810-818
[9]   Artificial intelligence and medical imaging 2018: French Radiology Community white paper [J].
Beregi, Jean-Paul .
DIAGNOSTIC AND INTERVENTIONAL IMAGING, 2018, 99 (11) :727-742
[10]   Application of breast tomosynthesis in screening: incremental effect on mammography acquisition and reading time [J].
Bernardi, D. ;
Ciatto, S. ;
Pellegrini, M. ;
Anesi, V. ;
Burlon, S. ;
Cauli, E. ;
Depaoli, M. ;
Larentis, L. ;
Malesani, V. ;
Targa, L. ;
Baldo, P. ;
Houssami, N. .
BRITISH JOURNAL OF RADIOLOGY, 2012, 85 (1020) :E1174-E1178