Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf

Introduction: Miniscrews as anchorage devices are being increasingly used by orthodontists. Cortical thickness is a major factor affecting the success of miniscrew placement. Orthodontists treat patients with different sagittal skeletal relations. Some clinicians use three-dimensional imaging for as...

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Main Author: Abdullah Al-Jaf, Nagham Mohammed
Format: Thesis
Language:English
Published: 2019
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Online Access:https://ir.uitm.edu.my/id/eprint/82833/1/82833.pdf
https://ir.uitm.edu.my/id/eprint/82833/
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Institution: Universiti Teknologi Mara
Language: English
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spelling my.uitm.ir.828332024-02-02T03:00:52Z https://ir.uitm.edu.my/id/eprint/82833/ Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf Abdullah Al-Jaf, Nagham Mohammed Back propagation (Artificial intelligence) Neural networks (Computer science) Introduction: Miniscrews as anchorage devices are being increasingly used by orthodontists. Cortical thickness is a major factor affecting the success of miniscrew placement. Orthodontists treat patients with different sagittal skeletal relations. Some clinicians use three-dimensional imaging for assessment of cortical thickness for miniscrew placement. Objectives: To assess buccal cortical thickness, interradicular distance and palatal cortical thickness in different sagittal skeletal relationship. The other objective of this study was to formulate a prediction model for buccal cortical thickness without exposing patients to three-dimensional imaging and high radiation dose. Methods: Archived cone beam computed tomography (CBCT) scans of 240 adult subjects with Class I, II and III sagittal skeletal relationship and normal vertical relation were used. The scans were divided into three groups of 80 subjects with equal gender distribution. Buccal cortical thickness and interradicular distance were measured in the alveolar processes of the maxilla and mandible. The sites measured were from between central incisors to the site between the two molars. Palatal cortical thickness was also measured at nine locations. Analysis of variance (ANOVA) with post-hoc Tukey test was used with a significance level of p < 0.05 to detect differences between sagittal skeletal classes. 2019 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/82833/1/82833.pdf Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf. (2019) PhD thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/82833.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Back propagation (Artificial intelligence)
Neural networks (Computer science)
spellingShingle Back propagation (Artificial intelligence)
Neural networks (Computer science)
Abdullah Al-Jaf, Nagham Mohammed
Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf
description Introduction: Miniscrews as anchorage devices are being increasingly used by orthodontists. Cortical thickness is a major factor affecting the success of miniscrew placement. Orthodontists treat patients with different sagittal skeletal relations. Some clinicians use three-dimensional imaging for assessment of cortical thickness for miniscrew placement. Objectives: To assess buccal cortical thickness, interradicular distance and palatal cortical thickness in different sagittal skeletal relationship. The other objective of this study was to formulate a prediction model for buccal cortical thickness without exposing patients to three-dimensional imaging and high radiation dose. Methods: Archived cone beam computed tomography (CBCT) scans of 240 adult subjects with Class I, II and III sagittal skeletal relationship and normal vertical relation were used. The scans were divided into three groups of 80 subjects with equal gender distribution. Buccal cortical thickness and interradicular distance were measured in the alveolar processes of the maxilla and mandible. The sites measured were from between central incisors to the site between the two molars. Palatal cortical thickness was also measured at nine locations. Analysis of variance (ANOVA) with post-hoc Tukey test was used with a significance level of p < 0.05 to detect differences between sagittal skeletal classes.
format Thesis
author Abdullah Al-Jaf, Nagham Mohammed
author_facet Abdullah Al-Jaf, Nagham Mohammed
author_sort Abdullah Al-Jaf, Nagham Mohammed
title Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf
title_short Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf
title_full Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf
title_fullStr Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf
title_full_unstemmed Cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / Nagham Mohammed Abdullah Al-Jaf
title_sort cortical bone thickness in different sagittal skeletal relationship: assessment and predictive modelling using artificial neural network / nagham mohammed abdullah al-jaf
publishDate 2019
url https://ir.uitm.edu.my/id/eprint/82833/1/82833.pdf
https://ir.uitm.edu.my/id/eprint/82833/
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