Literature DB >> 32941736

Artificial intelligence in paediatric radiology: Future opportunities.

Natasha Davendralingam1, Neil J Sebire1,2, Owen J Arthurs1,3, Susan C Shelmerdine1,3.   

Abstract

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.

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Mesh:

Year:  2020        PMID: 32941736      PMCID: PMC7774693          DOI: 10.1259/bjr.20200975

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  77 in total

Review 1.  Artificial intelligence for precision education in radiology.

Authors:  Michael Tran Duong; Andreas M Rauschecker; Jeffrey D Rudie; Po-Hao Chen; Tessa S Cook; R Nick Bryan; Suyash Mohan
Journal:  Br J Radiol       Date:  2019-07-26       Impact factor: 3.039

2.  Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.

Authors:  Enhao Gong; John M Pauly; Max Wintermark; Greg Zaharchuk
Journal:  J Magn Reson Imaging       Date:  2018-02-13       Impact factor: 4.813

3.  Deep Learning Measurement of Leg Length Discrepancy in Children Based on Radiographs.

Authors:  Qiang Zheng; Sphoorti Shellikeri; Hao Huang; Misun Hwang; Raymond W Sze
Journal:  Radiology       Date:  2020-04-21       Impact factor: 11.105

4.  Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study.

Authors:  Jens Kleesiek; Jan Nikolas Morshuis; Fabian Isensee; Katerina Deike-Hofmann; Daniel Paech; Philipp Kickingereder; Ullrich Köthe; Carsten Rother; Michael Forsting; Wolfgang Wick; Martin Bendszus; Heinz-Peter Schlemmer; Alexander Radbruch
Journal:  Invest Radiol       Date:  2019-10       Impact factor: 6.016

Review 5.  Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality.

Authors:  Xuan V Nguyen; Murat Alp Oztek; Devi D Nelakurti; Christina L Brunnquell; Mahmud Mossa-Basha; David R Haynor; Luciano M Prevedello
Journal:  Top Magn Reson Imaging       Date:  2020-08

6.  Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage.

Authors:  Daniel T Ginat
Journal:  Neuroradiology       Date:  2019-12-11       Impact factor: 2.804

7.  Machine-Learning-Based Prediction of a Missed Scheduled Clinical Appointment by Patients With Diabetes.

Authors:  Hisashi Kurasawa; Katsuyoshi Hayashi; Akinori Fujino; Koichi Takasugi; Tsuneyuki Haga; Kayo Waki; Takashi Noguchi; Kazuhiko Ohe
Journal:  J Diabetes Sci Technol       Date:  2016-05-03

8.  Problems and preferences in pediatric imaging.

Authors:  Brij Bhushan Thukral
Journal:  Indian J Radiol Imaging       Date:  2015 Oct-Dec

9.  Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

Authors:  Ana Luiza Dallora; Peter Anderberg; Ola Kvist; Emilia Mendes; Sandra Diaz Ruiz; Johan Sanmartin Berglund
Journal:  PLoS One       Date:  2019-07-25       Impact factor: 3.240

10.  The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study.

Authors:  Tao Chen; Ye Chen; Mengxue Yuan; Mark Gerstein; Tingyu Li; Huiying Liang; Tanya Froehlich; Long Lu
Journal:  JMIR Med Inform       Date:  2020-05-08
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  3 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

Review 2.  The current and future roles of artificial intelligence in pediatric radiology.

Authors:  Jeffrey P Otjen; Michael M Moore; Erin K Romberg; Francisco A Perez; Ramesh S Iyer
Journal:  Pediatr Radiol       Date:  2021-05-27

Review 3.  [Artificial intelligence in image evaluation and diagnosis].

Authors:  Hans-Joachim Mentzel
Journal:  Monatsschr Kinderheilkd       Date:  2021-07-02       Impact factor: 0.323

  3 in total

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