eprintid: 10113199
rev_number: 22
eprint_status: archive
userid: 608
dir: disk0/10/11/31/99
datestamp: 2020-10-27 17:17:56
lastmod: 2021-12-05 01:06:20
status_changed: 2020-10-27 17:17:56
type: article
metadata_visibility: show
creators_name: Davendralingam, N
creators_name: Sebire, NJ
creators_name: Arthurs, OJ
creators_name: Shelmerdine, SC
title: Artificial intelligence in paediatric radiology: Future opportunities
ispublished: pub
subjects: GOSH
divisions: UCL
divisions: B02
divisions: D13
divisions: G26
divisions: G25
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2020-09-17
date_type: published
official_url: https://doi.org/10.1259/bjr.20200975
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1814548
doi: 10.1259/bjr.20200975
lyricists_name: Arthurs, Owen
lyricists_name: Sebire, Neil
lyricists_name: Shelmerdine, Susan
lyricists_id: OARTH57
lyricists_id: NJSEB45
lyricists_id: SCSHE38
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: The British Journal of Radiology
volume: 94
number: 1117
article_number: 20200975
event_location: England
citation:        Davendralingam, N;    Sebire, NJ;    Arthurs, OJ;    Shelmerdine, SC;      (2020)    Artificial intelligence in paediatric radiology: Future opportunities.                   The British Journal of Radiology , 94  (1117)    , Article 20200975.  10.1259/bjr.20200975 <https://doi.org/10.1259/bjr.20200975>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10113199/1/bjr.20200975.pdf