Literature DB >> 33987993

Deep Learning-Based Artificial Intelligence for Mammography.

Jung Hyun Yoon1, Eun Kyung Kim2.   

Abstract

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.
Copyright © 2021 The Korean Society of Radiology.

Entities:  

Keywords:  Artificial intelligence; Breast cancer; Computer-aided diagnosis; Deep learning; Mammography

Year:  2021        PMID: 33987993     DOI: 10.3348/kjr.2020.1210

Source DB:  PubMed          Journal:  Korean J Radiol        ISSN: 1229-6929            Impact factor:   3.500


  7 in total

1.  Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x.

Authors:  Yun-Woo Chang; Jung Kyu Ryu; Jin Kyung An; Nami Choi; Kyung Hee Ko; Ki Hwan Kim; Kyunghwa Han
Journal:  J Breast Cancer       Date:  2022-01-06       Impact factor: 3.588

2.  Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis.

Authors:  Ga Eun Park; Bong Joo Kang; Sung Hun Kim; Jeongmin Lee
Journal:  Diagnostics (Basel)       Date:  2022-02-02

Review 3.  Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.

Authors:  Aimilia Gastounioti; Shyam Desai; Vinayak S Ahluwalia; Emily F Conant; Despina Kontos
Journal:  Breast Cancer Res       Date:  2022-02-20       Impact factor: 8.408

4.  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

5.  Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study.

Authors:  Jeong Hoon Lee; Ki Hwan Kim; Eun Hye Lee; Jong Seok Ahn; Jung Kyu Ryu; Young Mi Park; Gi Won Shin; Young Joong Kim; Hye Young Choi
Journal:  Korean J Radiol       Date:  2022-04-04       Impact factor: 7.109

6.  A pilot study on deep learning-based grading of corners of vertebral bodies for assessment of radiographic progression in patients with ankylosing spondylitis.

Authors:  Bon San Koo; Jae Joon Lee; Jae-Woo Jung; Chang Ho Kang; Kyung Bin Joo; Tae-Hwan Kim; Seunghun Lee
Journal:  Ther Adv Musculoskelet Dis       Date:  2022-07-22       Impact factor: 3.625

7.  External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women.

Authors:  Aimilia Gastounioti; Mikael Eriksson; Eric A Cohen; Walter Mankowski; Lauren Pantalone; Sarah Ehsan; Anne Marie McCarthy; Despina Kontos; Per Hall; Emily F Conant
Journal:  Cancers (Basel)       Date:  2022-09-30       Impact factor: 6.575

  7 in total

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