Literature DB >> 35523530

Updates in Artificial Intelligence for Breast Imaging.

Manisha Bahl1.   

Abstract

Artificial intelligence (AI) for breast imaging has rapidly moved from the experimental to implementation phase. As of this writing, Food and Drug Administration (FDA)-approved mammographic applications are available for triage, lesion detection and classification, and breast density assessment. For sonography and MRI, FDA-approved applications are available for lesion classification. Numerous other interpretive and noninterpretive AI applications are in the development phase. This article reviews AI applications for mammography, sonography, and MRI that are currently available for clinical use. In addition, clinical implementation and the future of AI for breast imaging are discussed.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2021        PMID: 35523530      PMCID: PMC9077006          DOI: 10.1053/j.ro.2021.12.005

Source DB:  PubMed          Journal:  Semin Roentgenol        ISSN: 0037-198X            Impact factor:   0.709


  41 in total

1.  Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions.

Authors:  Ross W Filice; John Mongan; Marc D Kohli
Journal:  J Am Coll Radiol       Date:  2020-10-06       Impact factor: 5.532

2.  National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium.

Authors:  Constance D Lehman; Robert F Arao; Brian L Sprague; Janie M Lee; Diana S M Buist; Karla Kerlikowske; Louise M Henderson; Tracy Onega; Anna N A Tosteson; Garth H Rauscher; Diana L Miglioretti
Journal:  Radiology       Date:  2016-12-05       Impact factor: 11.105

Review 3.  Screening breast ultrasound: past, present, and future.

Authors:  Rachel F Brem; Megan J Lenihan; Jennifer Lieberman; Jessica Torrente
Journal:  AJR Am J Roentgenol       Date:  2015-02       Impact factor: 3.959

4.  Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.

Authors:  Jionghui Gu; Tong Tong; Chang He; Min Xu; Xin Yang; Jie Tian; Tianan Jiang; Kun Wang
Journal:  Eur Radiol       Date:  2021-10-15       Impact factor: 5.315

5.  Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams.

Authors:  Yiqiu Shen; Farah E Shamout; Jamie R Oliver; Jan Witowski; Kawshik Kannan; Jungkyu Park; Nan Wu; Connor Huddleston; Stacey Wolfson; Alexandra Millet; Robin Ehrenpreis; Divya Awal; Cathy Tyma; Naziya Samreen; Yiming Gao; Chloe Chhor; Stacey Gandhi; Cindy Lee; Sheila Kumari-Subaiya; Cindy Leonard; Reyhan Mohammed; Christopher Moczulski; Jaime Altabet; James Babb; Alana Lewin; Beatriu Reig; Linda Moy; Laura Heacock; Krzysztof J Geras
Journal:  Nat Commun       Date:  2021-09-24       Impact factor: 17.694

6.  Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

Authors:  Ashirbani Saha; Lars J Grimm; Sujata V Ghate; Connie E Kim; Mary S Soo; Sora C Yoon; Maciej A Mazurowski
Journal:  J Magn Reson Imaging       Date:  2019-01-16       Impact factor: 4.813

Review 7.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       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.  To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines).

Authors:  Patrick Omoumi; Alexis Ducarouge; Antoine Tournier; Hugh Harvey; Charles E Kahn; Fanny Louvet-de Verchère; Daniel Pinto Dos Santos; Tobias Kober; Jonas Richiardi
Journal:  Eur Radiol       Date:  2021-03-05       Impact factor: 5.315

Review 10.  Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging.

Authors:  Anke Meyer-Bäse; Lia Morra; Uwe Meyer-Bäse; Katja Pinker
Journal:  Contrast Media Mol Imaging       Date:  2020-08-28       Impact factor: 3.161

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