Literature DB >> 31336151

Volumetric characteristics of prognathic mandible revealed by skeletal unit analysis.

Seong Ho Mun1, Mira Park2, Jun Lee1, Hun Jun Lim1, Bong Chul Kim3.   

Abstract

The purpose of this study was to evaluate the skeletal units of a normal mandible (class I) and a prognathic mandible (class III), to compare the groups, and to investigate the key functional unit responsible for mandibular prognathism. Hemi-mandibles of 101 cases were evaluated by cone-beam computed tomography. Of these, 50 cases had Class I and 51 had Class III mandibles. The length, volume, and volume/length ratio of each skeletal unit were measured. The ratios of the condyle, body unit, and sum of the hemi-mandible between Class I and Class III showed statistically significant results (P<0.05). However, the ratios of angle, coronoid, and symphysis units did not show any statistical significance on comparison. Dependent on gender, in males the ratio of the condyle of the hemi-mandible showed statistically significant results (P<0.05). Meanwhile in females the ratio of the body and sum of the hemi-mandible showed statistically significant results (P<0.05). Accordingly, the mandibular body and condylar units are thinner in mandibular prognathism. On the basis of the functional matrix theory to determine the aetiology of mandibular prognathism, the key skeletal units are the body and condylar units.
Copyright © 2019 Elsevier GmbH. All rights reserved.

Entities:  

Keywords:  Cone-beam computed tomography; Mandible; Prognathism

Mesh:

Year:  2019        PMID: 31336151     DOI: 10.1016/j.aanat.2019.07.007

Source DB:  PubMed          Journal:  Ann Anat        ISSN: 0940-9602            Impact factor:   2.698


  6 in total

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Authors:  Carolin Olbrisch; Petra Santander; Norman Moser; Daniela Klenke; Philipp Meyer-Marcotty; Anja Quast
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2.  Volumetric Change in the Masseter and Lateral Pterygoid after Mandibular Setback.

Authors:  Jae Hyun Kang; Dong Sun Shin; See Woon Kim; Hun Jun Lim; Bong Chul Kim
Journal:  J Pers Med       Date:  2022-05-18

3.  Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network.

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Journal:  J Pers Med       Date:  2022-06-18

4.  Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals.

Authors:  WooSang Shin; Han-Gyeol Yeom; Ga Hyung Lee; Jong Pil Yun; Seung Hyun Jeong; Jong Hyun Lee; Hwi Kang Kim; Bong Chul Kim
Journal:  BMC Oral Health       Date:  2021-03-18       Impact factor: 2.757

5.  Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs.

Authors:  Seung Hyun Jeong; Jong Pil Yun; Han-Gyeol Yeom; Hun Jun Lim; Jun Lee; Bong Chul Kim
Journal:  Sci Rep       Date:  2020-10-01       Impact factor: 4.379

6.  Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography.

Authors:  Seung Hyun Jeong; Jong Pil Yun; Han-Gyeol Yeom; Hwi Kang Kim; Bong Chul Kim
Journal:  Diagnostics (Basel)       Date:  2021-03-25
  6 in total

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