• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

骨龄仪(BoneXpert)与IB-Lab-PANDA在生长发育和青春期障碍儿童中进行自动骨龄评估的比较。

Comparison of BoneXpert and IB-Lab-PANDA automated Bone Age Evaluation in Children With Growth and Puberty Disorders.

作者信息

Ruiz-Arana Inge-Lore, Lechanteur Victor, Busiah Kanetee, Bouthors Thérèse, Antoniou Maria-Christina, Stoppa-Vaucher Sophie, Ruspa Martina, Alamo Leonor, Hauschild Michael

机构信息

Pediatric Endocrinology, Diabetes and Obesity Unit, Department Woman-Mother-Child, Lausanne University Hospital, Lausanne 1004, Switzerland.

School of Medicine, Faculty of Biology and Medicine, University of Lausanne, Lausanne 1015, Switzerland.

出版信息

J Endocr Soc. 2025 Jul 21;9(9):bvaf122. doi: 10.1210/jendso/bvaf122. eCollection 2025 Sep.

DOI:10.1210/jendso/bvaf122
PMID:40862089
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12375918/
Abstract

CONTEXT

Bone age (BA) evaluation in children presenting growth problems is time-consuming. Artificial intelligence (AI) BA assessment programs are increasingly used. However, agreement between different commercially available methods in the same population, or possible age-, puberty- or sex-related differences have not been sufficiently evaluated.

METHODS

BA assessment of 521 left hand radiographs of patients aged 2-19 years with IB-lab-PANDA® and BoneXpert® were compared. Of the 521 radiographs, 213 were compared to the Greulich-Pyle (GP) reference. We analyzed gender, age, diagnosis, body mass index (BMI) and puberty categories. Accuracy was calculated as mean-absolute-deviation (MAD) and root-mean-square-error (RMSE).

RESULTS

MAD was 0.61 years and the RMSE 0.83 years between BoneXpert and IB-lab-PANDA, with poor agreement in girls over 14 years (MAD 1.18 years).Compared to the manual rating, both methods showed a positive bias in boys (0.28 years BoneXpert vs 0.51 years IB-lab-PANDA) and in children with pathologies associated with BA delay (BoneXpert 0.18 years vs IB-lab-PANDA 0.35 years). IB-lab-PANDA underestimated BA in girls after 14 years (-0.67 years). IB-lab-PANDA had a MAD of 0.64 years and RMSE of 0.85 years compared to manual assessment, whereas BoneXpert had a MAD of 0.63 years and RMSE of 0.82 years.BoneXpert was significantly more accurate than IB-lab-PANDA in prepubertal children (MAD 0.7 vs 0.83 years; = .027).

CONCLUSION

The direct agreement between IB-lab-PANDA® and BoneXpert® falls within human inter-rater variability. Their agreement on manual BA determination is equivalent except in prepubertal children, where BoneXpert seems more accurate. Both are fast, valuable tools for determining BA accurately and efficiently.

摘要

背景

对存在生长问题的儿童进行骨龄(BA)评估耗时较长。人工智能(AI)骨龄评估程序的使用越来越广泛。然而,同一人群中不同市售方法之间的一致性,以及可能存在的与年龄、青春期或性别相关的差异尚未得到充分评估。

方法

对521例年龄在2至19岁患者的左手X光片分别采用IB-lab-PANDA®和BoneXpert®进行骨龄评估,并进行比较。在这521张X光片中,213张与格鲁利希-派尔(GP)参考标准进行了对比。我们分析了性别、年龄、诊断结果、体重指数(BMI)和青春期类别。准确性通过平均绝对偏差(MAD)和均方根误差(RMSE)来计算。

结果

BoneXpert和IB-lab-PANDA之间的MAD为0.61岁,RMSE为0.83岁,14岁以上女孩的一致性较差(MAD为1.18岁)。与人工评级相比,两种方法在男孩中均表现出正偏差(BoneXpert为0.28岁,IB-lab-PANDA为0.51岁),在与骨龄延迟相关疾病的儿童中也表现出正偏差(BoneXpert为0.18岁,IB-lab-PANDA为0.35岁)。IB-lab-PANDA在14岁后的女孩中低估了骨龄(-0.67岁)。与人工评估相比,IB-lab-PANDA的MAD为0.64岁,RMSE为0.85岁,而BoneXpert的MAD为0.63岁,RMSE为0.82岁。在青春期前儿童中,BoneXpert比IB-lab-PANDA显著更准确(MAD分别为0.7岁和0.83岁;P = 0.027)。

结论

IB-lab-PANDA®和BoneXpert®之间的直接一致性在人类评分者间的变异性范围内。它们在人工骨龄测定方面的一致性相当,除了青春期前儿童,在这一群体中BoneXpert似乎更准确。两者都是准确、高效测定骨龄的快速且有价值的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/ea4a3003e782/bvaf122f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/d81c44746298/bvaf122f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/034544f7b42d/bvaf122f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/ea4d3aca1a5c/bvaf122f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/4955fdec97a6/bvaf122f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/ea4a3003e782/bvaf122f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/d81c44746298/bvaf122f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/034544f7b42d/bvaf122f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/ea4d3aca1a5c/bvaf122f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/4955fdec97a6/bvaf122f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f51/12375918/ea4a3003e782/bvaf122f5.jpg

相似文献

1
Comparison of BoneXpert and IB-Lab-PANDA automated Bone Age Evaluation in Children With Growth and Puberty Disorders.骨龄仪(BoneXpert)与IB-Lab-PANDA在生长发育和青春期障碍儿童中进行自动骨龄评估的比较。
J Endocr Soc. 2025 Jul 21;9(9):bvaf122. doi: 10.1210/jendso/bvaf122. eCollection 2025 Sep.
2
Comparative analysis of an automated bone age tool with manual assessment in a multiethnic Southeast Asian paediatric cohort in Singapore.新加坡多民族东南亚儿科队列中自动骨龄工具与人工评估的比较分析。
Pediatr Radiol. 2025 Aug 28. doi: 10.1007/s00247-025-06374-4.
3
Prescription of Controlled Substances: Benefits and Risks管制药品的处方:益处与风险
4
Automated devices for identifying peripheral arterial disease in people with leg ulceration: an evidence synthesis and cost-effectiveness analysis.用于识别下肢溃疡患者外周动脉疾病的自动化设备:证据综合和成本效益分析。
Health Technol Assess. 2024 Aug;28(37):1-158. doi: 10.3310/TWCG3912.
5
Artificial intelligence algorithm improves radiologists' bone age assessment accuracy.人工智能算法提高了放射科医生的骨龄评估准确性。
J Chin Med Assoc. 2025 Jul 1;88(7):530-537. doi: 10.1097/JCMA.0000000000001248. Epub 2025 May 15.
6
Falls prevention interventions for community-dwelling older adults: systematic review and meta-analysis of benefits, harms, and patient values and preferences.社区居住的老年人跌倒预防干预措施:系统评价和荟萃分析的益处、危害以及患者的价值观和偏好。
Syst Rev. 2024 Nov 26;13(1):289. doi: 10.1186/s13643-024-02681-3.
7
Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.两种现代生存预测工具 SORG-MLA 和 METSSS 在接受手术联合放疗和单纯放疗治疗有症状长骨转移患者中的比较。
Clin Orthop Relat Res. 2024 Dec 1;482(12):2193-2208. doi: 10.1097/CORR.0000000000003185. Epub 2024 Jul 23.
8
Effectiveness and safety of vitamin D in relation to bone health.维生素D对骨骼健康的有效性与安全性。
Evid Rep Technol Assess (Full Rep). 2007 Aug(158):1-235.
9
Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.在基层医疗机构或医院门诊环境中,如果患者出现以下症状和体征,可判断其是否患有 COVID-19。
Cochrane Database Syst Rev. 2022 May 20;5(5):CD013665. doi: 10.1002/14651858.CD013665.pub3.
10
Systemic pharmacological treatments for chronic plaque psoriasis: a network meta-analysis.系统性药理学治疗慢性斑块状银屑病:网络荟萃分析。
Cochrane Database Syst Rev. 2021 Apr 19;4(4):CD011535. doi: 10.1002/14651858.CD011535.pub4.

本文引用的文献

1
Bone mineral density in childhood cancer survivors during and after oncological treatment: A systematic review and meta-analysis.肿瘤治疗期间及之后儿童癌症幸存者的骨矿物质密度:一项系统评价与荟萃分析。
Osteoporos Int. 2025 May;36(5):767-777. doi: 10.1007/s00198-025-07458-5. Epub 2025 Mar 27.
2
A critical comparative study of the performance of three AI-assisted programs for bone age determination.三种人工智能辅助骨龄测定程序性能的关键比较研究。
Eur Radiol. 2025 Mar;35(3):1190-1196. doi: 10.1007/s00330-024-11169-6. Epub 2024 Nov 5.
3
Automated bone age assessment in a German pediatric cohort: agreement between an artificial intelligence software and the manual Greulich and Pyle method.
德国儿科队列中的自动骨龄评估:人工智能软件与手动 Greulich 和 Pyle 方法的一致性。
Eur Radiol. 2024 Jul;34(7):4407-4413. doi: 10.1007/s00330-023-10543-0. Epub 2023 Dec 28.
4
Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.Deeplasia:骨骼发育不良中验证的深度学习骨龄评估。
Pediatr Radiol. 2024 Jan;54(1):82-95. doi: 10.1007/s00247-023-05789-1. Epub 2023 Nov 13.
5
A comprehensive validation study of the latest version of BoneXpert on a large cohort of Caucasian children and adolescents.对最新版本的 BoneXpert 在一大群白种人儿童和青少年中的全面验证研究。
Front Endocrinol (Lausanne). 2023 Mar 24;14:1130580. doi: 10.3389/fendo.2023.1130580. eCollection 2023.
6
Accuracy and self-validation of automated bone age determination.骨龄自动测定的准确性和自我验证。
Sci Rep. 2022 Apr 16;12(1):6388. doi: 10.1038/s41598-022-10292-y.
7
Validation of automated bone age analysis from hand radiographs in a North American pediatric population.北美儿科人群手部 X 光片的自动骨龄分析验证。
Pediatr Radiol. 2022 Jun;52(7):1347-1355. doi: 10.1007/s00247-022-05310-0. Epub 2022 Mar 24.
8
Artificial Intelligence-Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience.人工智能辅助骨龄评估以提高不同经验水平医生的准确性和一致性。
Front Pediatr. 2022 Feb 24;10:818061. doi: 10.3389/fped.2022.818061. eCollection 2022.
9
Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment.儿科放射学中的自主人工智能:BoneXpert 用于骨龄评估的使用和感知。
Pediatr Radiol. 2022 Jun;52(7):1338-1346. doi: 10.1007/s00247-022-05295-w. Epub 2022 Feb 28.
10
Low bone mass in Noonan syndrome children correlates with decreased muscle mass and low IGF-1 levels.努南综合征患儿的低骨量与肌肉量减少和 IGF-1 水平降低有关。
Bone. 2021 Dec;153:116170. doi: 10.1016/j.bone.2021.116170. Epub 2021 Sep 4.