Literature DB >> 33067733

The Greulich-Pyle and Gilsanz-Ratib atlas method versus automated estimation tool for bone age: a multi-observer agreement study.

Ural Koc1, Onur Taydaş2,3, Semih Bolu4, Atilla Halil Elhan5, S Pınar Karakas6.   

Abstract

PURPOSE: To evaluate the agreement between observers using Greulich-Pyle (GP) and Gilsanz-Ratib (GR) methods, between four specialities (radiology, pediatrics, pediatric endocrinology and pediatric radiology) and between observers and automated tool in the bone age estimation.
MATERIALS AND METHODS: A total of 99 observers participated in this questionnaire-based study. BoneXpert was used for the automated tool. Experienced, senior, and junior observers were defined by their experience, and the bone age determined by experienced observers was regarded as the ground truth. Agreement between observers was evaluated using the coefficient of variance (CV) and intraclass correlation coefficient (ICC), and they were reevaluated after adding BoneXpert to the observers. Agreement of BoneXpert, the senior, and the junior observers was also evaluated using the root-mean-square-error (RMSE) values and Blant Altman method by comparing with the ground truth.
RESULTS: The CV ranged from 4.98% to 22.08%. The ICC were 0.980 for GP, 0.980 for GP and BoneXpert, 0.973 for GR, and 0.976 for GR and BoneXpert, and the ICC between four specialities ranged form 0.963 to 0.990. BoneXpert tool had the lowest RMSE values (0.504 years for GP atlas).
CONCLUSION: Automated bone age estimation showed comparable results with GP and GR methods and its utilization may decrease inter-observer variability.

Keywords:  Automated bone age estimation; Digital radiography; GP; GR; Pediatrics

Mesh:

Year:  2020        PMID: 33067733     DOI: 10.1007/s11604-020-01055-8

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN: 1867-1071            Impact factor:   2.374


  1 in total

1.  Height predictions by Bayley-Pinneau method may misguide pediatric endocrinologists.

Authors:  Omer Tarım
Journal:  Turk J Pediatr       Date:  2013 Sep-Oct       Impact factor: 0.552

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1.  Artificial Intelligence-Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience.

Authors:  Xi Wang; Bo Zhou; Ping Gong; Ting Zhang; Yan Mo; Jie Tang; Xinmiao Shi; Jianhong Wang; Xinyu Yuan; Fengsen Bai; Lei Wang; Qi Xu; Yu Tian; Qing Ha; Chencui Huang; Yizhou Yu; Lin Wang
Journal:  Front Pediatr       Date:  2022-02-24       Impact factor: 3.418

  1 in total

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