Literature DB >> 27169673

Age estimation based on pelvic ossification using regression models from conventional radiography.

Kui Zhang1, Xiao-Ai Dong1, Fei Fan1, Zhen-Hua Deng2.   

Abstract

To establish regression models for age estimation from the combination of the ossification of iliac crest and ischial tuberosity. One thousand three hundred and seventy-nine conventional pelvic radiographs at the West China Hospital of Sichuan University between January 2010 and June 2012 were evaluated retrospectively. The receiver operating characteristic analysis was performed to measure the value of estimation of 18 years of age with the classification scheme for the iliac crest and ischial tuberosity. Regression analysis was performed, and formulas for calculating approximate chronological age according to the combination developmental status of the ossification for the iliac crest and ischial tuberosity were developed. The areas under the receiver operating characteristic (ROC) curves were above 0.9 (p < 0.001), indicating a good prediction of the grading systems, and the cubic regression model was found to have the highest R-square value (R (2) = 0.744 for female and R (2) = 0.753 for male). The present classification scheme for apophyseal iliac crest ossification and the ischial tuberosity may be used for age estimation. And the present established cubic regression model according to the combination developmental status of the ossification for the iliac crest and ischial tuberosity can be used for age estimation.

Entities:  

Keywords:  Age estimation; Iliac crest; Ischial tuberosity; Ossification; Regression analysis

Mesh:

Year:  2016        PMID: 27169673     DOI: 10.1007/s00414-016-1383-7

Source DB:  PubMed          Journal:  Int J Legal Med        ISSN: 0937-9827            Impact factor:   2.686


  29 in total

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7.  Skeletal age determination of the hand: a comparison of methods.

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2.  Computed tomographic evaluation of the acetabulum for age estimation in an Indian population using principal component analysis and regression models.

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