Computer Assisted Bone Age Estimation Using Dimensions of Metacarpal Bones and Metacarpophalangeal Joints Based on Neural Network.
Statement of the Problem: Bone age is a more accurate assessment for biologic development than chronological age. The most common method for bone age estimation is using Pyle and Greulich Atlas. Today, computer-based techniques are becoming more favorable among investigators. However, the morphologi...
| Published in: | Journal of Dentistry (2345-6485) Vol. 25; no. 1; pp. 51 - 59 |
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| Main Authors: | , , , |
| Format: | diagnostic images research tables/charts Journal Article |
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Shiraz University of Medical Sciences
Mar2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175840301&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175840301 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23456485 MX2K jtl: Journal of Dentistry (2345-6485) issn: 23456485 maglogo: N pubinfo: dt: Mar2024 vid: 25 iid: 1 pid: 65846 pub: Shiraz University of Medical Sciences place: Shiraz, <Blank> artinfo: ui: 175840301 175840301 175840301 10.30476/dentjods.2023.95629.1882 175840301 ppf: 51 ppct: 8 formats: tig: atl: Computer Assisted Bone Age Estimation Using Dimensions of Metacarpal Bones and Metacarpophalangeal Joints Based on Neural Network. aug: au: Haghnegahdar, Abdolaziz Pakshir, Hamid Reza Zandieh, Mojtaba Ghanbari, Ilnaz affil: Dept. of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran. sug: subj: Age Determination by Skeleton Methods Neural Networks (Computer) Methods Metacarpal Bones Radiography Metacarpophalangeal Joint Radiography Human Male Female Child, Preschool Child Adolescence Cross Sectional Studies Retrospective Design Paired T-Tests Intraclass Correlation Coefficient Radiography Methods Data Analysis Software Descriptive Statistics Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Male Female ab: Statement of the Problem: Bone age is a more accurate assessment for biologic development than chronological age. The most common method for bone age estimation is using Pyle and Greulich Atlas. Today, computer-based techniques are becoming more favorable among investigators. However, the morphological features in Greulich and Pyle method are difficult to be converted into quantitative measures. During recent years, metacarpal bones and metacarpophalangeal joints dimensions were shown to be highly correlated with skeletal age. Purpose: In this study, we have evaluated the accuracy and reliability of a trained neural network for bone age estimation with quantitative and recently introduced related data, including chronological age, height, trunk height, weight, metacarpal bones, and metacarpophalangeal joints dimensions. Materials and Method: In this cross sectional retrospective study, aneural network, using MATLAB, was utilized to determine bone age by employing quantitative features for 304 subjects. To evaluate the accuracy of age estimation software, paired t-test, and inter-class correlation was used. Results: The difference between the mean bone ages determined by the radiologists and the mean bone ages assessed by the age estimation software was not significant (p Value= 0.119 in male subjects and p= 0.922 in female subjects). The results from the software and radiologists showed a strong correlation -ICC=0.990 in male subjects and ICC=0.986 in female subjects (p< 0.001). Conclusion: The results have shown an acceptable accuracy in bone age estimation with training neural network and using dimensions of bones and joints. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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