Literature DB >> 31706569

Applicability of two bone age assessment methods to children from Saudi Arabia.

K Alshamrani1, A Hewitt2, A C Offiah3.   

Abstract

AIM: To assess the applicability of the Greulich & Pyle (G&P) and Tanner & Whitehouse (TW3) methods to children from Saudi Arabia using both subjective (manual) rating and BoneXpert software.
MATERIALS AND METHODS: Bone age (BA) was assessed using the G&P and TW3 methods, firstly by independent manual rating of two observers, followed by a single observer using the BoneXpert software program. In total, 420 hand trauma radiographs for Saudi Arabians (220 males, 329 left, age range 1-18 years) performed in the period January 2012 to September 2016 were assessed. Paired sample t test was used to compare the difference between mean BA and mean chronological age (CA) and to compare the difference between manual and BoneXpert ratings. Statistical analysis was undertaken using SPSS v.25.
RESULTS: A statistically significant difference was found between BA and CA in males when using the G&P (mean difference -0.36±1 years, p<0.01) and TW3 (mean difference -0.22±0.9 years, p=0.03) methods, but not in females for either G&P (mean difference 0.13±1.2 years) or TW3 (mean difference 0.08±1.1 years). In males, BoneXpert results conformed to the manual ratings for TW3, but not for G&P, for which the mean difference between manual and BoneXpert ratings was -0.27±0.5 years (p<0.01). DISCUSSION: The present results indicate that manual and BoneXpert-derived G&P and TW3 bone age assessment can be applied with no modification to Saudi Arabian females; however, only TW3 BoneXpert-derived BA can be applied without caution to Saudi Arabian males. Crown
Copyright © 2019. Published by Elsevier Ltd. All rights reserved.

Entities:  

Year:  2019        PMID: 31706569     DOI: 10.1016/j.crad.2019.08.029

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  3 in total

1.  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

2.  Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs.

Authors:  Jarno T Huhtanen; Mikko Nyman; Dorin Doncenco; Maral Hamedian; Davis Kawalya; Leena Salminen; Roberto Blanco Sequeiros; Seppo K Koskinen; Tomi K Pudas; Sami Kajander; Pekka Niemi; Jussi Hirvonen; Hannu J Aronen; Mojtaba Jafaritadi
Journal:  Sci Rep       Date:  2022-07-12       Impact factor: 4.996

Review 3.  Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology.

Authors:  Amaka C Offiah
Journal:  Pediatr Radiol       Date:  2021-07-16
  3 in total

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