Artificial intelligence in paediatric radiology: Future opportunities

被引:27
|
作者
Davendralingam, Natasha [1 ]
Sebire, Neil J. [1 ,2 ]
Arthurs, Owen J. [1 ,3 ]
Shelmerdine, Susan C. [1 ,3 ]
机构
[1] Great Ormond St Hosp Children NHS Fdn Trust, Dept Radiol, London, England
[2] Great Ormond St Hosp Children NHS Fdn Trust, Dept Histopathol, London, England
[3] UCL Great Ormond St Inst Child Hlth, London, England
来源
BRITISH JOURNAL OF RADIOLOGY | 2021年 / 94卷 / 1117期
基金
英国医学研究理事会;
关键词
NEURAL-NETWORKS;
D O I
10.1259/bjr.20200975
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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.
引用
收藏
页数:9
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