Literature DB >> 28514006

The FELS method of assessing the skeletal maturity of the hand-wrist.

Wm Cameron Chumela1,2, Alex F Roche1,2, David Thissen1,2.   

Abstract

The FELS method of assessing the skeletal maturity of the hand-wrist differs from the Greulich-Pyle and Tanner-Whitehouse methods in the observations made, the provision of a range of shapes to which maturity indicator grades can be assigned and in the statistical methods used to construct the scale of skeletal maturity. The FELS method for the hand-wrist was developed using 13,823 serial radiographs of the left hand-wrist of boys and girls in the Fels Longitudinal Study. One hundred-thirty possible indicators were originally identified. Eighty-five graded and 13 metric indicators were selected on the basis of an indicator's ability to discriminate between children at the same chronological age, its universal appearance, reliability, validity, and completeness. The subset of FEELS maturity indicators assessed at a chronological age are analyzed with a microcomputer program that provides the skeletal age and the standard error of the estimate for that skeletal age. Comparison among hand-wrist skeletal age assessments for children in the Fels Longitudinal Study by the FELS, Greulich-Pyle, and Tanner-Whitehouse methods indicate that the FELS method is the more appropriate method for the present population of United States children.
Copyright © 1989 Wiley-Liss, Inc., A Wiley Company.

Entities:  

Year:  1989        PMID: 28514006     DOI: 10.1002/ajhb.1310010206

Source DB:  PubMed          Journal:  Am J Hum Biol        ISSN: 1042-0533            Impact factor:   1.937


  11 in total

Review 1.  [Classification of the growth potential and consecutive treatment consequences for spinal deformities : When does what make sense?]

Authors:  M Thielen; M Akbar
Journal:  Orthopade       Date:  2019-06       Impact factor: 1.087

2.  The More the Merrier: Integrating Multiple Models of Skeletal Maturity Improves the Accuracy of Growth Prediction.

Authors:  Alana M Munger; Kristin E Yu; Don T Li; Ryan J Furdock; Melanie E Boeyer; Dana L Duren; David R Weber; Daniel R Cooperman
Journal:  J Pediatr Orthop       Date:  2021-08       Impact factor: 2.537

3.  The influence of maturation, fitness, and hormonal indices on minutes played in elite youth soccer players: a cross-sectional study.

Authors:  Ebrahim Eskandarifard; Hadi Nobari; Filipe Manuel Clemente; Rui Silva; Cain C T Clark; Hugo Sarmento; António José Figueiredo
Journal:  BMC Sports Sci Med Rehabil       Date:  2022-05-17

4.  Delays in maturation among adolescents with hemophilia and a history of inhibitors.

Authors:  Sharyne M Donfield; Henry S Lynn; Alice E Lail; W Keith Hoots; Erik Berntorp; Edward D Gomperts
Journal:  Blood       Date:  2007-08-22       Impact factor: 22.113

5.  Probing an AI regression model for hand bone age determination using gradient-based saliency mapping.

Authors:  Zhiyue J Wang
Journal:  Sci Rep       Date:  2021-05-19       Impact factor: 4.379

6.  The current state of forensic age estimation of live subjects for the purpose of criminal prosecution.

Authors:  Andreas Schmeling; Walter Reisinger; Gunther Geserick; Andreas Olze
Journal:  Forensic Sci Med Pathol       Date:  2005-12       Impact factor: 2.456

7.  Traditional and New Methods of Bone Age Assessment-An Overview

Authors:  Monika Prokop-Piotrkowska; Kamila Marszałek-Dziuba; Elżbieta Moszczyńska; Mieczysław Szalecki; Elżbieta Jurkiewicz
Journal:  J Clin Res Pediatr Endocrinol       Date:  2020-10-26

8.  Associations between match participation, maturation, physical fitness, and hormonal levels in elite male soccer player U15: a prospective study with observational cohort.

Authors:  Ebrahim Eskandarifard; Hadi Nobari; Filipe Manuel Clemente; Rui Silva; Ana Filipa Silva; Antonio José Figueiredo
Journal:  BMC Pediatr       Date:  2022-04-11       Impact factor: 2.125

9.  Physical characteristics of elite youth male football players aged 13-15 are based upon biological maturity.

Authors:  Shidong Yang; Haichun Chen
Journal:  PeerJ       Date:  2022-05-05       Impact factor: 3.061

10.  Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment.

Authors:  Hans Henrik Thodberg; Benjamin Thodberg; Joanna Ahlkvist; Amaka C Offiah
Journal:  Pediatr Radiol       Date:  2022-02-28
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