Literature DB >> 30704666

How far have we come? Artificial intelligence for chest radiograph interpretation.

K Kallianos1, J Mongan1, S Antani2, T Henry1, A Taylor1, J Abuya3, M Kohli4.   

Abstract

Due to recent advances in artificial intelligence, there is renewed interest in automating interpretation of imaging tests. Chest radiographs are particularly interesting due to many factors: relatively inexpensive equipment, importance to public health, commonly performed throughout the world, and deceptively complex taking years to master. This article presents a brief introduction to artificial intelligence, reviews the progress to date in chest radiograph interpretation, and provides a snapshot of the available datasets and algorithms available to chest radiograph researchers. Finally, the limitations of artificial intelligence with respect to interpretation of imaging studies are discussed.
Copyright © 2019 The Royal College of Radiologists. All rights reserved.

Mesh:

Year:  2019        PMID: 30704666     DOI: 10.1016/j.crad.2018.12.015

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  26 in total

Review 1.  Advanced imaging tools for childhood tuberculosis: potential applications and research needs.

Authors:  Sanjay K Jain; Savvas Andronikou; Pierre Goussard; Sameer Antani; David Gomez-Pastrana; Christophe Delacourt; Jeffrey R Starke; Alvaro A Ordonez; Patrick Jean-Philippe; Renee S Browning; Carlos M Perez-Velez
Journal:  Lancet Infect Dis       Date:  2020-06-23       Impact factor: 25.071

2.  Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.

Authors:  Pir Masoom Shah; Faizan Ullah; Dilawar Shah; Abdullah Gani; Carsten Maple; Yulin Wang; Mohammad Abrar; Saif Ul Islam
Journal:  IEEE Access       Date:  2021-05-05       Impact factor: 3.476

3.  Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images.

Authors:  Jia Liu; Jing Qi; Wei Chen; Yongjian Nian
Journal:  Comput Biol Med       Date:  2022-06-15       Impact factor: 6.698

4.  Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images.

Authors:  Mohammad Salehi; Reza Mohammadi; Hamed Ghaffari; Nahid Sadighi; Reza Reiazi
Journal:  Br J Radiol       Date:  2021-04-16       Impact factor: 3.039

5.  Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists.

Authors:  Alan A Peters; Amanda Decasper; Jaro Munz; Jeremias Klaus; Laura I Loebelenz; Maximilian Korbinian Michael Hoffner; Cynthia Hourscht; Johannes T Heverhagen; Andreas Christe; Lukas Ebner
Journal:  J Thorac Dis       Date:  2021-05       Impact factor: 3.005

6.  Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns.

Authors:  Karen Panetta; Foram Sanghavi; Sos Agaian; Neel Madan
Journal:  IEEE J Biomed Health Inform       Date:  2021-06-03       Impact factor: 7.021

7.  Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment.

Authors:  Chellammal Surianarayanan; Pethuru Raj Chelliah
Journal:  New Gener Comput       Date:  2021-06-10       Impact factor: 1.048

8.  Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases.

Authors:  Abdullahi Umar Ibrahim; Mehmet Ozsoz; Sertan Serte; Fadi Al-Turjman; Salahudeen Habeeb Kolapo
Journal:  Expert Syst       Date:  2021-04-26       Impact factor: 2.812

9.  Augmenting Interpretation of Chest Radiographs With Deep Learning Probability Maps.

Authors:  Brian Hurt; Andrew Yen; Seth Kligerman; Albert Hsiao
Journal:  J Thorac Imaging       Date:  2020-09       Impact factor: 5.528

10.  Deep learning based detection of COVID-19 from chest X-ray images.

Authors:  Sarra Guefrechi; Marwa Ben Jabra; Adel Ammar; Anis Koubaa; Habib Hamam
Journal:  Multimed Tools Appl       Date:  2021-07-19       Impact factor: 2.757

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