Evidence map›Paper›PMID 40301164›Full record

ArticleEuropean journal of pediatrics2025

AI-powered automated X-ray bone age analyzer for assisting bone age assessment in Chinese children and adolescents: accuracy, consistency, and time efficiency.

Yan Liang, Xiumin Wang, Xiao Bo Chen, Zhe Su, Xin Ran Cheng, Xiao Ping Luo

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yan LiangDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Xiumin Wang *Department of Endocrinology and Metabolism, Shanghai Children'S Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Xiao Bo Chen *Department of Endocrinology, Children'S Hospital, Capital Institute of Pediatrics, Beijing, 100020, China.
Zhe Su *Department of Endocrinology, Shenzhen Children'S Hospital, No. 7019, Yitian Road, Shenzhen, 518038, China.
Xin Ran Cheng *Department of Pediatric Endocrine Genetics and Metabolism, Chengdu Women'S and Children'S Center Hospital, Chengdu, 610074, China.
Xiao Ping LuoDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. xpluo@tjh.tjmu.edu.cn.ORCID http://orcid.org/0000-0002-3748-4817

Funding

National Key Research and Development Program of China 2023YFC2706300
6 · The paper itself

Abstract

This study aims to validate the auxiliary effect of a commercially available AI-powered X-ray bone age analyzer for physicians and to evaluate its impact on the accuracy, consistency, and time efficiency of bone age assessment in Chinese children and adolescents. We conducted a multicenter, prospective study involving 1000 children aged 1 to 18 years across five centers in China. Radiographs were independently assessed for TW3-RUS and TW3-Carpal bone age by five physicians-three juniors and two seniors-both without and with AI assistance. An additional AI group was included, relying solely on the AI analyzer. With AI assistance, the mean absolute error for TW3-RUS ranged from 0.34 to 0.49 for different raters, while for TW3-Carpal, it ranged from 0.32 to 0.46. The mean squared error was between 0.20 and 0.40 for TW3-RUS and between 0.18 and 0.36 for TW3-Carpal. The accuracy varied from 88.20 to 96.80% for TW3-RUS and from 90.60 to 97.20% for TW3-Carpal. Intraclass correlation coefficients exceeded 0.98 for both TW3-RUS and TW3-Carpal. For both the TW3-RUS and TW3-Carpal methods, junior raters exhibited statistically reduced error and increased accuracy with AI assistance (all P < 0.0001). The assistance of AI was also found to significantly benefit senior raters in specific age groups. Evaluation time was also significantly reduced with AI assistance (all P < 0.0001).

conclusionIncorporating AI into BAA enhances accuracy and consistency, especially for junior raters, while reducing evaluation time. Senior raters also benefit in specific age groups. WHAT IS KNOWN: • Bone age assessment (BAA) is crucial for diagnosing and monitoring endocrine and growth disorders in children, traditionally relying on manual interpretation of radiographs, which is time-consuming and subject to variability. WHAT IS NEW: • The incorporation of an AI-powered X-ray bone age analyzer significantly enhances the accuracy and consistency of BAA in Chinese children and adolescents, particularly benefiting junior raters while reducing evaluation time.

Indexed as

Age Determination by SkeletonAdolescentChildChild, PreschoolChinaEast Asian PeopleFemaleHumansInfantMaleProspective StudiesRadiographyReproducibility of ResultsAge determination by skeletonArtificial intelligenceBone ageRadiographyTanner-Whitehouse 3 method

Identifiers

PMID40301164

What OpenQuestion holds

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Registered trials

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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.