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作者

Yan Liang, Xiaobo Chen, Rongxiu Zheng, Xinran Cheng, Zhe Su, Xiumin Wang, Hongwei Du, Min Zhu, Guimei Li, Yan Zhong, Shengquan Cheng, Baosheng Yu, Yu Yang, Ruimin Chen, Lanwei Cui, Hui Yao, Qiang Gu, Chunxiu Gong, Zhang Jun, Xiaoyan Huang, Deyun Liu, Xueqin Yan, Haiyan Wei, Yuwen Li, Huifeng Zhang, Yanjie Liu, Fengyun Wang, Gaixiu Zhang, Xin Fan, Hongmei Dai, Xiaoping Luo

作者单位

Affiliations ¹ Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. ² Hubei Key Laboratory of Pediatric Genetic Metabolic and Endocrine Rare Diseases, Wuhan, 430030, China. ³ Department of Endocrinology, Children's Hospital, Capital Institute of Pediatrics, Beijing, 100020, China. ⁴ Department of Pediatrics, Tianjin Medical University General Hospital, Tianjin, 300052, China. ⁵ Department of Pediatric Endocrine Genetics and Metabolism, Chengdu Women's and Children's Center Hospital, Chengdu, 610074, China. ⁶ Department of Endocrinology, Shenzhen Children's Hospital, No. 7019 Yitian Road, Shenzhen, 518038, China. ⁷ Department of Endocrinology and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China. ⁸ Department of Paediatrics, First Hospital of Jilin University, Changchun, 130021, China. ⁹ Department of Endocrinology, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China. ¹⁰ Department of Pediatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China. ¹¹ Department of Child Health Care, Hunan Children's Hospital, Changsha, 410007, China. ¹² Department of Pediatrics, First Affiliated Hospital of Air Force Medical University, Xi'an, 710032, China. ¹³ Department of Pediatrics, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, 210003, China. ¹⁴ Department of Endocrinology and Genetics, Jiangxi Provincial Children's Hospital, Affiliated Children's Hospital of Nanchang University, Nanchang, 330006, China. ¹⁵ Department of Endocrinology, Genetics and Metabolism, Fuzhou Children's Hospital of Fujian Medical University, Fuzhou, 350005, China. ¹⁶ Department of Pediatric, The First Affiliated Hospital of Harbin Medical University, Harbin, 150007, China. ¹⁷ Department of Endocrinology and Metabolism, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430015, China. ¹⁸ Department of Pediatrics, First Affiliated Hospital of Shihezi University, Shihezi, 832000, China. ¹⁹ Department of Endocrine and Genetics and Metabolism, Beijing Children's Hospital, Capital Medical University, National Centre for Children's Health, Beijing, 100045, China. ²⁰ Department of Pediatrics, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China. ²¹ Department of Genetics, Metabolism and Endocrinology, Hainan Women and Children's Medical Center, Haikou, 570312, China. ²² Department of Pediatrics, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China. ²³ Department of Pediatrics, Boai Hospital of Zhongshan, Zhongshan, 528400, China. ²⁴ Department of Endocrinology and Metabolism, Genetics, Henan Children's Hospital (Children's Hospital Affiliated to Zhengzhou University), Zhengzhou, 450018, China. ²⁵ Department of Pediatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. ²⁶ Department of Pediatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China. ²⁷ Department of Pediatrics, Inner Mongolia People's Hospital, Hohhot, 010017, China. ²⁸ Department of Endocrinology, Children's Hospital of Soochow University, Suzhou, 215025, China. ²⁹ Department of Endocrine and Genetics and Metabolism, Children's Hospital of Shanxi, Taiyuan, 030006, China. ³⁰ Department of Pediatric, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 537406, China. ³¹ Department of Pediatric, The Third Xiangya Hospital, Central South University, Changsha, 410013, China. ³² Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. xpluo@tjh.tjmu.edu.cn. ³³ Hubei Key Laboratory of Pediatric Genetic Metabolic and Endocrine Rare Diseases, Wuhan, 430030, China. xpluo@tjh.tjmu.edu.cn.

刊名

Advances in therapy

年份

2024

卷号

Vol.41 No.9

页码

3664-3677

ISSN

1865-8652

关键词

Age determination by skeleton Artificial intelligence Bone age Radiography Tanner–Whitehouse 3 method.

摘要

Introduction: Automated bone age assessment 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 -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 method. Methods: Radiographs prospectively collected from 30 centers across various regions in China, inc...更多

Introduction: Automated bone age assessment 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 -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 method. Methods: Radiographs prospectively collected from 30 centers across various regions in China, including 900 Chinese children and adolescents, were assessed independently by six doctors and an AI analyzer for TW3 radius, ulna, and short bones 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. Results: For the estimation of TW3-RUS, the AI analyzer had a mean absolute error 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 ; however, it was not significant for rater 2 or rater 3 . 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 and numerically lower for rater 1 and rater 3 . These results were consistent for the subgroups according to sex, and differences between the age groups were observed. Conclusion: In 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.收起

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