Literature DB >> 33844048

Current and emerging artificial intelligence applications for pediatric abdominal imaging.

Jonathan R Dillman1,2, Elan Somasundaram3,4, Samuel L Brady3,4, Lili He3,4.   

Abstract

Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep learning (e.g., convolutional neural networks) are two commonly employed AI approaches both outside and within the field of medicine. Such techniques can be used to evaluate medical images for the purposes of automated detection and segmentation, classification tasks (including diagnosis, lesion or tissue characterization, and prediction), and image reconstruction. In this review article we highlight recent literature describing current and emerging AI methods applied to abdominal imaging (e.g., CT, MRI and US) and suggest potential future applications of AI in the pediatric population.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Abdomen; Artificial intelligence; Children; Computed tomography; Deep learning; Machine learning; Magnetic resonance imaging

Mesh:

Year:  2021        PMID: 33844048     DOI: 10.1007/s00247-021-05057-0

Source DB:  PubMed          Journal:  Pediatr Radiol        ISSN: 0301-0449


  46 in total

1.  Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.

Authors:  James H Thrall; Xiang Li; Quanzheng Li; Cinthia Cruz; Synho Do; Keith Dreyer; James Brink
Journal:  J Am Coll Radiol       Date:  2018-02-04       Impact factor: 5.532

Review 2.  Deep learning in neural networks: an overview.

Authors:  Jürgen Schmidhuber
Journal:  Neural Netw       Date:  2014-10-13

Review 3.  A survey on deep learning in medical image analysis.

Authors:  Geert Litjens; Thijs Kooi; Babak Ehteshami Bejnordi; Arnaud Arindra Adiyoso Setio; Francesco Ciompi; Mohsen Ghafoorian; Jeroen A W M van der Laak; Bram van Ginneken; Clara I Sánchez
Journal:  Med Image Anal       Date:  2017-07-26       Impact factor: 8.545

Review 4.  Deep learning in medical imaging and radiation therapy.

Authors:  Berkman Sahiner; Aria Pezeshk; Lubomir M Hadjiiski; Xiaosong Wang; Karen Drukker; Kenny H Cha; Ronald M Summers; Maryellen L Giger
Journal:  Med Phys       Date:  2018-11-20       Impact factor: 4.071

Review 5.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

6.  Machine Learning in Medical Imaging.

Authors:  Miles N Wernick; Yongyi Yang; Jovan G Brankov; Grigori Yourganov; Stephen C Strother
Journal:  IEEE Signal Process Mag       Date:  2010-07       Impact factor: 12.551

Review 7.  Radiomics: extracting more information from medical images using advanced feature analysis.

Authors:  Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts
Journal:  Eur J Cancer       Date:  2012-01-16       Impact factor: 9.162

Review 8.  Deep Learning in Medical Image Analysis.

Authors:  Dinggang Shen; Guorong Wu; Heung-Il Suk
Journal:  Annu Rev Biomed Eng       Date:  2017-03-09       Impact factor: 9.590

9.  Computational Radiomics System to Decode the Radiographic Phenotype.

Authors:  Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

Review 10.  Machine and deep learning methods for radiomics.

Authors:  Michele Avanzo; Lise Wei; Joseph Stancanello; Martin Vallières; Arvind Rao; Olivier Morin; Sarah A Mattonen; Issam El Naqa
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

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  1 in total

1.  European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age.

Authors:  Lene Bjerke Laborie; Jaishree Naidoo; Erika Pace; Pierluigi Ciet; Christine Eade; Matthias W Wagner; Thierry A G M Huisman; Susan C Shelmerdine
Journal:  Pediatr Radiol       Date:  2022-06-22
  1 in total

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