Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment

Author:

Thodberg Hans Henrik,Thodberg Benjamin,Ahlkvist Joanna,Offiah Amaka C.ORCID

Abstract

Abstract Background The autonomous artificial intelligence (AI) system for bone age rating (BoneXpert) was designed to be used in clinical radiology practice as an AI-replace tool, replacing the radiologist completely. Objective The aim of this study was to investigate how the tool is used in clinical practice. Are radiologists more inclined to use BoneXpert to assist rather than replace themselves, and how much time is saved? Materials and methods We sent a survey consisting of eight multiple-choice questions to 282 radiologists in departments in Europe already using the software. Results The 97 (34%) respondents came from 18 countries. Their answers revealed that before installing the automated method, 83 (86%) of the respondents took more than 2 min per bone age rating; this fell to 20 (21%) respondents after installation. Only 17/97 (18%) respondents used BoneXpert to completely replace the radiologist; the rest used it to assist radiologists to varying degrees. For instance, 39/97 (40%) never overruled the automated reading, while 9/97 (9%) overruled more than 5% of the automated ratings. The majority 58/97 (60%) of respondents checked the radiographs themselves to exclude features of underlying disease. Conclusion BoneXpert significantly reduces reporting times for bone age determination. However, radiographic analysis involves more than just determining bone age. It also involves identification of abnormalities, and for this reason, radiologists cannot be completely replaced. AI systems originally developed to replace the radiologist might be more suitable as AI assist tools, particularly if they have not been validated to work autonomously, including the ability to omit ratings when the image is outside the range of validity.

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Pediatrics, Perinatology and Child Health

Reference32 articles.

1. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

2. An understanding of AI’s limitations is starting to sink in. The Economist. https://www.economist.com/technology-quarterly/2020/06/11/an-understanding-of-ais-limitations-is-starting-to-sink-in. Accessed 14 Apr 2021

3. Gallix B, Chong J (2019) Artificial intelligence in radiology: who’s afraid of the big bad wolf? Eur Radiol 29:1637–1639

4. Langlotz CP (2019) Will artificial intelligence replace radiologists? Radiol Artif Intell 1:e190058

5. van Ginneken B (2018) Talk at ECR 2018: AI and radiologists — a painful divorce? Vimeo. https://vimeo.com/258232453. Accessed 10 Jan 2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3