Literature DB >> 32803154

Artificial Intelligence: A Primer for Breast Imaging Radiologists.

Manisha Bahl1.   

Abstract

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 ultimately ensure optimal patient care. © Society of Breast Imaging 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  artificial intelligence; breast imaging; deep learning; machine learning; mammography

Year:  2020        PMID: 32803154      PMCID: PMC7418877          DOI: 10.1093/jbi/wbaa033

Source DB:  PubMed          Journal:  J Breast Imaging        ISSN: 2631-6110


  77 in total

1.  Workforce shortages in breast imaging: impact on mammography utilization.

Authors:  Paul Wing; Margaret H Langelier
Journal:  AJR Am J Roentgenol       Date:  2009-02       Impact factor: 3.959

Review 2.  Digital image analysis in breast pathology-from image processing techniques to artificial intelligence.

Authors:  Stephanie Robertson; Hossein Azizpour; Kevin Smith; Johan Hartman
Journal:  Transl Res       Date:  2017-11-07       Impact factor: 7.012

3.  Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.

Authors:  Shahein H Tajmir; Tarik K Alkasab
Journal:  Acad Radiol       Date:  2018-03-26       Impact factor: 3.173

4.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

5.  Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms.

Authors:  Ayelet Akselrod-Ballin; Michal Chorev; Yoel Shoshan; Adam Spiro; Alon Hazan; Roie Melamed; Ella Barkan; Esma Herzel; Shaked Naor; Ehud Karavani; Gideon Koren; Yaara Goldschmidt; Varda Shalev; Michal Rosen-Zvi; Michal Guindy
Journal:  Radiology       Date:  2019-06-18       Impact factor: 11.105

6.  Variability in the Management Recommendations Given for High-risk Breast Lesions Detected on Image-guided Core Needle Biopsy at U.S. Academic Institutions.

Authors:  Eniola Falomo; Catherine Adejumo; Kathryn A Carson; Susan Harvey; Lisa Mullen; Kelly Myers
Journal:  Curr Probl Diagn Radiol       Date:  2018-06-27

7.  Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Authors:  Alejandro Rodríguez-Ruiz; Elizabeth Krupinski; Jan-Jurre Mordang; Kathy Schilling; Sylvia H Heywang-Köbrunner; Ioannis Sechopoulos; Ritse M Mann
Journal:  Radiology       Date:  2018-11-20       Impact factor: 11.105

8.  Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

Authors:  Li-Qiang Zhou; Xing-Long Wu; Shu-Yan Huang; Ge-Ge Wu; Hua-Rong Ye; Qi Wei; Ling-Yun Bao; You-Bin Deng; Xing-Rui Li; Xin-Wu Cui; Christoph F Dietrich
Journal:  Radiology       Date:  2019-11-19       Impact factor: 11.105

9.  A Deep Learning Model to Triage Screening Mammograms: A Simulation Study.

Authors:  Adam Yala; Tal Schuster; Randy Miles; Regina Barzilay; Constance Lehman
Journal:  Radiology       Date:  2019-08-06       Impact factor: 11.105

Review 10.  Artificial intelligence in cancer imaging: Clinical challenges and applications.

Authors:  Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts
Journal:  CA Cancer J Clin       Date:  2019-02-05       Impact factor: 508.702

View more
  3 in total

Review 1.  Updates in Artificial Intelligence for Breast Imaging.

Authors:  Manisha Bahl
Journal:  Semin Roentgenol       Date:  2021-12-31       Impact factor: 0.709

2.  Artificial Intelligence for Breast Ultrasound: Will It Impact Radiologists' Accuracy?

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2021-04-26

3.  Research Highlight: Artificial Intelligence for Ruling Out Negative Examinations in Screening Breast MRI.

Authors:  Ji Hyun Youk; Eun-Kyung Kim
Journal:  Korean J Radiol       Date:  2022-02       Impact factor: 3.500

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.