Evidence map›Paper›PMID 39085749›Full record

ArticleAdvances in therapy2024

Validation of an AI-Powered Automated X-ray Bone Age Analyzer in Chinese Children and Adolescents: A Comparison with the Tanner-Whitehouse 3 Method.

Yan Liang, Xiaobo Chen, Rongxiu Zheng, Xinran Cheng, Zhe Su, Xiumin Wang, Hongwei Du, Min Zhu, Guimei Li, Yan Zhong and 21 more

Abstract readValidation StudyComparative StudyMulticenter Study
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In one paragraph

Article in Advances in therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

31 authors.

Yan LiangDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Xiaobo ChenDepartment of Endocrinology, Children's Hospital, Capital Institute of Pediatrics, Beijing, 100020, China.
Rongxiu ZhengDepartment of Pediatrics, Tianjin Medical University General Hospital, Tianjin, 300052, China.
Xinran ChengDepartment of Pediatric Endocrine Genetics and Metabolism, Chengdu Women's and Children's Center Hospital, Chengdu, 610074, China.
Zhe SuDepartment of Endocrinology, Shenzhen Children's Hospital, No. 7019 Yitian Road, Shenzhen, 518038, China.
Xiumin WangDepartment of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Hongwei DuDepartment of Paediatrics, First Hospital of Jilin University, Changchun, 130021, China.
Min ZhuDepartment of Endocrinology, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China.
Guimei LiDepartment of Pediatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China.
Yan ZhongDepartment of Child Health Care, Hunan Children's Hospital, Changsha, 410007, China.
Shengquan ChengDepartment of Pediatrics, First Affiliated Hospital of Air Force Medical University, Xi'an, 710032, China.
Baosheng YuDepartment of Pediatrics, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, 210003, China.
Yu YangDepartment of Endocrinology and Genetics, Jiangxi Provincial Children's Hospital, Affiliated Children's Hospital of Nanchang University, Nanchang, 330006, China.
Ruimin ChenDepartment of Endocrinology, Genetics and Metabolism, Fuzhou Children's Hospital of Fujian Medical University, Fuzhou, 350005, China.
Lanwei CuiDepartment of Pediatric, The First Affiliated Hospital of Harbin Medical University, Harbin, 150007, China.
Hui YaoDepartment of Endocrinology and Metabolism, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430015, China.
Qiang GuDepartment of Pediatrics, First Affiliated Hospital of Shihezi University, Shihezi, 832000, China.
Chunxiu GongDepartment of Endocrine and Genetics and Metabolism, Beijing Children's Hospital, Capital Medical University, National Centre for Children's Health, Beijing, 100045, China.
Zhang JunDepartment of Pediatrics, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China.
Xiaoyan HuangDepartment of Genetics, Metabolism and Endocrinology, Hainan Women and Children's Medical Center, Haikou, 570312, China.
Deyun LiuDepartment of Pediatrics, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.
Xueqin YanDepartment of Pediatrics, Boai Hospital of Zhongshan, Zhongshan, 528400, China.
Haiyan WeiDepartment of Endocrinology and Metabolism, Genetics, Henan Children's Hospital (Children's Hospital Affiliated to Zhengzhou University), Zhengzhou, 450018, China.
Yuwen LiDepartment of Pediatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China.
Huifeng ZhangDepartment of Pediatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
Yanjie LiuDepartment of Pediatrics, Inner Mongolia People's Hospital, Hohhot, 010017, China.
Fengyun WangDepartment of Endocrinology, Children's Hospital of Soochow University, Suzhou, 215025, China.
Gaixiu ZhangDepartment of Endocrine and Genetics and Metabolism, Children's Hospital of Shanxi, Taiyuan, 030006, China.
Xin FanDepartment of Pediatric, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 537406, China.
Hongmei DaiDepartment of Pediatric, The Third Xiangya Hospital, Central South University, Changsha, 410013, China.
Xiaoping LuoDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. xpluo@tjh.tjmu.edu.cn.ORCID 0000-0002-3748-4817

Funding

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

Abstract

introductionAutomated bone age assessment (BAA) is of growing interest because of its accuracy and time efficiency in daily practice. In this study, we validated the clinical applicability of a commercially available artificial intelligence (AI)-powered X-ray bone age analyzer equipped with a deep learning-based automated BAA system and compared its performance with that of the Tanner-Whitehouse 3 (TW-3) method.

methodsRadiographs prospectively collected from 30 centers across various regions in China, including 900 Chinese children and adolescents, were assessed independently by six doctors (three experts and three residents) and an AI analyzer for TW3 radius, ulna, and short bones (RUS) and TW3 carpal bone age. The experts' mean estimates were accepted as the gold standard. The performance of the AI analyzer was compared with that of each resident.

resultsFor the estimation of TW3-RUS, the AI analyzer had a mean absolute error (MAE) of 0.48 ± 0.42. The percentage of patients with an absolute error of < 1.0 years was 86.78%. The MAE was significantly lower than that of rater 1 (0.54 ± 0.49, P = 0.0068); however, it was not significant for rater 2 (0.48 ± 0.48) or rater 3 (0.49 ± 0.46). For TW3 carpal, the AI analyzer had an MAE of 0.48 ± 0.65. The percentage of patients with an absolute error of < 1.0 years was 88.78%. The MAE was significantly lower than that of rater 2 (0.58 ± 0.67, P = 0.0018) and numerically lower for rater 1 (0.54 ± 0.64) and rater 3 (0.50 ± 0.53). These results were consistent for the subgroups according to sex, and differences between the age groups were observed.

conclusionIn this comprehensive validation study conducted in China, an AI-powered X-ray bone age analyzer showed accuracies that matched or exceeded those of doctor raters. This method may improve the efficiency of clinical routines by reducing reading time without compromising accuracy.

Indexed as

Age Determination by SkeletonAdolescentArtificial IntelligenceChildChild, PreschoolChinaEast Asian PeopleFemaleHumansInfantMaleProspective StudiesReproducibility of ResultsAge determination by skeletonArtificial intelligenceBone ageRadiographyTanner–Whitehouse 3 method

Identifiers

PMID39085749

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