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Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development.人工智能系统在对生长发育异常的中国儿童进行骨龄评估时,能够取得与专家相当的结果。
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2
Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system.基于卷积神经网络的坦纳-怀特豪斯3型骨龄评估系统的诊断性能
Quant Imaging Med Surg. 2020 Mar;10(3):657-667. doi: 10.21037/qims.2020.02.20.
3
The RSNA Pediatric Bone Age Machine Learning Challenge.RSNA 儿科骨龄机器学习挑战赛。
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4
Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability.人工智能辅助解读骨龄X光片可提高准确性并减少变异性。
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Current Applications and Future Impact of Machine Learning in Radiology.机器学习在放射学中的当前应用和未来影响。
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Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: a systematic analysis from the Global Burden of Disease Study 2016.衡量 195 个国家和地区及部分次国家级地点的医疗卫生可得性和质量指数的表现:来自 2016 年全球疾病负担研究的系统分析。
Lancet. 2018 Jun 2;391(10136):2236-2271. doi: 10.1016/S0140-6736(18)30994-2. Epub 2018 Jun 1.
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Deep learning for automated skeletal bone age assessment in X-ray images.深度学习在 X 射线图像中进行自动骨骼骨龄评估。
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西藏地区骨龄评估人工智能系统的性能表现。

Performance of an artificial intelligence system for bone age assessment in Tibet.

机构信息

Department of Radiology, Peking Union Medical College Hospital, Beijing, China.

Department of Radiology, Tibet Autonomous Region People's Hospital, Lhasa, China.

出版信息

Br J Radiol. 2021 Apr 1;94(1120):20201119. doi: 10.1259/bjr.20201119. Epub 2021 Feb 9.

DOI:10.1259/bjr.20201119
PMID:33560889
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8010542/
Abstract

OBJECTIVE

To investigate whether bone age (BA) of children living in Tibet Highland could be accurately assessed using a fully automated artificial intelligence (AI) system.

METHODS

: Left hand radiographs of 385 children (300 Tibetan and 85 immigrant Han) aged 4-18 years who presented to the largest medical center of Tibet between September 2013 and November 2019 were consecutively collected. From these radiographs, BA was determined using the Greulich and Pyle (GP) method by experts in a consensus manner; furthermore, BA was estimated by a previously reported artificial intelligence (AI) BA system based on Han children from southern China. The performance of the AI system was compared with that of experts by using statistical analysis.

RESULTS

Compared with the experts' results, the accuracy of the AI system for Tibetan and Han children within 1 year was 84.67 and 89.41%, respectively, and its mean absolute difference (MAD) was 0.65 and 0.56 years, respectively. The discrepancy in hand-wrist bone maturation was the main cause of low accuracy of the system in the 4- to 6-year-old group.

CONCLUSION

The AI BA system developed for Han Chinese children living in flat regions could enable to assess BA accurately in Tibet where medical resources are limited.

ADVANCES IN KNOWLEDGE

AI-based BA system may serve as an effective and efficient solution to assess BA in Tibet.

摘要

目的

探讨使用全自动人工智能(AI)系统是否能准确评估生活在西藏高原的儿童的骨龄(BA)。

方法

连续收集了 2013 年 9 月至 2019 年 11 月期间在西藏最大医疗中心就诊的 385 名 4-18 岁儿童(300 名藏族和 85 名移民汉族)的左手 X 光片。这些 X 光片由专家通过共识方式使用 Greulich 和 Pyle(GP)方法确定 BA;此外,还使用来自中国南方汉族儿童的先前报道的人工智能(AI)BA 系统估算 BA。通过统计分析比较 AI 系统与专家的性能。

结果

与专家的结果相比,AI 系统对藏族和汉族儿童的准确率分别为 84.67%和 89.41%,平均绝对差(MAD)分别为 0.65 岁和 0.56 岁。系统在 4-6 岁组准确率较低的主要原因是手-腕骨成熟度的差异。

结论

为居住在平坦地区的汉族儿童开发的 AI 骨龄系统可以在医疗资源有限的西藏地区准确评估 BA。

知识进展

基于 AI 的 BA 系统可能是评估西藏 BA 的有效且高效的解决方案。