Artificial intelligence in paediatric radiology: international survey of health care professionals' opinions

被引:20
作者
Shelmerdine, Susan C. [1 ,2 ,3 ,4 ]
Rosendahl, Karen [5 ,6 ]
Arthurs, Owen J. [1 ,2 ,3 ]
机构
[1] Great Ormond St Hosp Sick Children, Dept Clin Radiol, London WC1N 3JH, England
[2] Great Ormond St Hosp Sick Children, UCL Great Ormond St Inst Child Hlth, London, England
[3] Great Ormond St Hosp NIHR Biomed Res Ctr, London, England
[4] St George Hosp, Dept Clin Radiol, London, England
[5] UiT Arctic Univ Norway, Dept Clin Med, Tromso, Norway
[6] Univ Hosp North Norway, Dept Radiol, Tromso, Norway
基金
英国医学研究理事会; 美国国家卫生研究院;
关键词
Artificial intelligence; Children; Imaging; Machine learning; Paediatric Radiology; Radiology; Survey; DISEASES;
D O I
10.1007/s00247-021-05195-5
中图分类号
R72 [儿科学];
学科分类号
100202 ;
摘要
Background The nature of paediatric radiology work poses several challenges for developing and implementing artificial intelligence (AI) tools, but opinions of those working in the field are currently unknown. Objective To evaluate the attitudes and perceptions toward AI amongst health care professionals working within children's imaging services. Materials and methods A web-based questionnaire was distributed to the membership of several paediatric and general radiological societies over a 4-month period (1 Feb - 31 May 2020). Survey questions covered attitudes toward AI in general, future impacts and suggested areas for development specifically within paediatric imaging. Results Two hundred and forty responses were collected with the majority being from radiologists (159/240, 66.3%; 95% confidence interval [CI] 59.8-72.2%) or allied health care professionals (72/240, 31.3%; 95% CI 25.4-37.5%). Respondents agreed that AI could potentially alert radiologists to imaging abnormalities (148/240, 61.7%; 95% CI 55.2-67.9%) but preferred that results were checked by a human (200/240, 83.3%; 95% CI 78.0-87.8%). The majority did not believe jobs in paediatric radiology would be replaced by AI (205/240, 85.4%; 95% CI 80.3-89.6%) and that the development of AI tools should focus on improved diagnostic accuracy (77/240, 32.1%; 95% CI 26.2-38.4%), workflow efficiencies (60/240, 25.0%; 95% CI 19.7-30.9%) and patient safety (54/240, 22.5%; 95% CI 17.4-28.3%). The majority of European Society of Paediatric Radiology (ESPR) members (67/81, 82.7%; 95% CI 72.7-90.2%) welcomed the idea of a dedicated paediatric radiology AI task force with emphasis on educational events and anonymised dataset curation. Conclusion Imaging health care professionals working with children had a positive outlook regarding the use of AI in paediatric radiology, and did not feel their jobs were threatened. Future AI tools would be most beneficial for easily automated tasks and most practitioners welcomed the opportunity for further AI educational activities.
引用
收藏
页码:30 / 41
页数:12
相关论文
共 26 条
[1]   Are semi-automated software program designed for adults accurate for the identification of vertebral fractures in children? [J].
Alqahtani, Fawaz F. ;
Messina, Fabrizio ;
Offiah, Amaka C. .
EUROPEAN RADIOLOGY, 2019, 29 (12) :6780-6789
[2]   External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules [J].
Baldwin, David R. ;
Gustafson, Jennifer ;
Pickup, Lyndsey ;
Arteta, Carlos ;
Novotny, Petr ;
Declerck, Jerome ;
Kadir, Timor ;
Figueiras, Catarina ;
Sterba, Albert ;
Exell, Alan ;
Potesil, Vaclav ;
Holland, Paul ;
Spence, Hazel ;
Clubley, Alison ;
O'Dowd, Emma ;
Clark, Matthew ;
Ashford-Turner, Victoria ;
Callister, Matthew E. J. ;
Gleeson, Fergus, V .
THORAX, 2020, 75 (04) :306-312
[3]   The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database [J].
Benjamens, Stan ;
Dhunnoo, Pranavsingh ;
Mesko, Bertalan .
NPJ DIGITAL MEDICINE, 2020, 3 (01)
[4]   Fears of an AI pioneer [J].
Bohannon, John .
SCIENCE, 2015, 349 (6245) :252-252
[5]   Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology [J].
Brkljacic, Boris ;
Derchi, Lorenzo E. ;
Hamm, Bernd ;
Fuchsjager, Michael ;
Krestin, Gabriel ;
Dewey, Marc ;
Parizel, Paul ;
Clark, Jonathan ;
Codari, Marina ;
Melazzini, Luca ;
Morozov, Sergey P. ;
van Kuijk, Cornelis C. ;
Sconfienza, Luca M. ;
Sardanelli, Francesco .
INSIGHTS INTO IMAGING, 2019, 10 (01)
[6]   Diagnosis of common pulmonary diseases in children by X-ray images and deep learning [J].
Chen, Kai-Chi ;
Yu, Hong-Ren ;
Chen, Wei-Shiang ;
Lin, Wei-Che ;
Lee, Yi-Chen ;
Chen, Hung-Hsun ;
Jiang, Jyun-Hong ;
Su, Ting-Yi ;
Tsai, Chang-Ku ;
Tsai, Ti-An ;
Tsai, Chih-Min ;
Lu, Henry Horng-Shing .
SCIENTIFIC REPORTS, 2020, 10 (01)
[7]   Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography [J].
Choi, Jae Won ;
Cho, Yeon Jin ;
Lee, Seowoo ;
Lee, Jihyuk ;
Lee, Seunghyun ;
Choi, Young Hun ;
Cheon, Jung-Eun ;
Ha, Ji Young .
INVESTIGATIVE RADIOLOGY, 2020, 55 (02) :101-110
[8]   Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey [J].
Coppola, Francesca ;
Faggioni, Lorenzo ;
Regge, Daniele ;
Giovagnoni, Andrea ;
Golfieri, Rita ;
Bibbolino, Corrado ;
Miele, Vittorio ;
Neri, Emanuele ;
Grassi, Roberto .
RADIOLOGIA MEDICA, 2021, 126 (01) :63-71
[9]   Artificial intelligence in paediatric radiology: Future opportunities [J].
Davendralingam, Natasha ;
Sebire, Neil J. ;
Arthurs, Owen J. ;
Shelmerdine, Susan C. .
BRITISH JOURNAL OF RADIOLOGY, 2021, 94 (1117)
[10]   AI for radiographic COVID-19 detection selects shortcuts over signal [J].
DeGrave, Alex J. ;
Janizek, Joseph D. ;
Lee, Su-In .
NATURE MACHINE INTELLIGENCE, 2021, 3 (07) :610-619