Literature DB >> 24481755

Chest radiography: new technological developments and their applications.

S Schalekamp1, B van Ginneken1, N Karssemeijer1, C M Schaefer-Prokop1.   

Abstract

Digital chest radiography is still the most common radiological examination. With the upcoming three-dimensional (3D) acquisition techniques the value of radiography seems to diminish. But because radiography is inexpensive, readily available, and requires very little dose, it is still being used for the first-line detection of many cardiothoracic diseases. In the last decades major technical developments of this 2D technique are being achieved. First, hardware developments of digital radiography have improved the contrast to noise, dose efficacy, throughput, and workflow. Dual energy acquisition techniques reduce anatomical noise by splitting a chest radiograph into a soft tissue image and a bone image. Second, advanced processing methods are developed to enable and improve detection of many kinds of disease. Digital bone subtraction by a software algorithm mimics the soft tissue image normally acquired with dedicated hardware. Temporal subtraction aims to rule out anatomical structures clotting the image, by subtracting a current radiograph with a previous radiograph. Finally, computer-aided detection systems help radiologists for the detection of various kinds of disease such as pulmonary nodules or tuberculosis. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

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Year:  2014        PMID: 24481755     DOI: 10.1055/s-0033-1363447

Source DB:  PubMed          Journal:  Semin Respir Crit Care Med        ISSN: 1069-3424            Impact factor:   3.119


  9 in total

1.  [Detection of lung nodules. New opportunities in chest radiography].

Authors:  S Pötter-Lang; S Schalekamp; C Schaefer-Prokop; M Uffmann
Journal:  Radiologe       Date:  2014-05       Impact factor: 0.635

2.  Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings.

Authors:  Sohee Park; Sang Min Lee; Kyung Hee Lee; Kyu-Hwan Jung; Woong Bae; Jooae Choe; Joon Beom Seo
Journal:  Eur Radiol       Date:  2019-11-20       Impact factor: 5.315

3.  Undetected Lung Cancer at Posteroanterior Chest Radiography: Potential Role of a Deep Learning-based Detection Algorithm.

Authors:  Ju Gang Nam; Eui Jin Hwang; Da Som Kim; Seung-Jin Yoo; Hyewon Choi; Jin Mo Goo; Chang Min Park
Journal:  Radiol Cardiothorac Imaging       Date:  2020-12-10

Review 4.  Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.

Authors:  Steven Schalekamp; Willemijn M Klein; Kicky G van Leeuwen
Journal:  Pediatr Radiol       Date:  2021-09-01

5.  Introduction to deep learning: minimum essence required to launch a research.

Authors:  Tomohiro Wataya; Katsuyuki Nakanishi; Yuki Suzuki; Shoji Kido; Noriyuki Tomiyama
Journal:  Jpn J Radiol       Date:  2020-06-15       Impact factor: 2.374

6.  High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

Authors:  Alvin Rajkomar; Sneha Lingam; Andrew G Taylor; Michael Blum; John Mongan
Journal:  J Digit Imaging       Date:  2017-02       Impact factor: 4.056

7.  Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.

Authors:  Eui Jin Hwang; Sunggyun Park; Kwang-Nam Jin; Jung Im Kim; So Young Choi; Jong Hyuk Lee; Jin Mo Goo; Jaehong Aum; Jae-Joon Yim; Julien G Cohen; Gilbert R Ferretti; Chang Min Park
Journal:  JAMA Netw Open       Date:  2019-03-01

8.  Efficiency and reporting confidence analysis of sequential dual-energy subtraction for thoracic x-ray examinations.

Authors:  Mehmet Can Gezer; Oktay Algin; Aytac Durmaz; Halil Arslan
Journal:  Qatar Med J       Date:  2019-09-23

Review 9.  Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.

Authors:  Eui Jin Hwang; Chang Min Park
Journal:  Korean J Radiol       Date:  2020-05       Impact factor: 3.500

  9 in total

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