Evidence map›Paper›PMID 33560889›Full record

ArticleThe British journal of radiology2021

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

Fengdan Wang, Wangjiu Cidan, Xiao Gu, Shi Chen, Wu Yin, Yongliang Liu, Lei Shi, Hui Pan, Zhengyu Jin

Abstract read
In one paragraph

Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Fengdan WangDepartment of Radiology, Peking Union Medical College Hospital, Beijing, China.ORCID https://orcid.org/0000-0002-1647-7634
Wangjiu CidanDepartment of Radiology, Tibet Autonomous Region People's Hospital, Lhasa, China.
Xiao GuDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing, China.
Shi ChenDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing, China.
Wu YinDepartment of Radiology, Tibet Autonomous Region People's Hospital, Lhasa, China.
Yongliang LiuHangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China.
Lei ShiHangzhou YITU Healthcare Technology Co., Ltd, Hangzhou, China.
Hui PanDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing, China.
Zhengyu JinDepartment of Radiology, Peking Union Medical College Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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.

resultsCompared 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.

conclusionThe 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.

Indexed as

Artificial IntelligenceAdolescentAge Determination by SkeletonChildChild, PreschoolFemaleHandHumansImage Processing, Computer-AssistedMaleRadiographyReproducibility of ResultsTibet

Identifiers

PMID33560889
PMCPMC8010542

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.