@article{Oh2022k,

title = {Prevalence of mandibular asymmetry in different skeletal sagittal patterns: A systematic review},

author = {Evangelista K and Teodoro A.B and Bianchi J and Cevidanes L.H.S and de Oliveria Ruellas A.C and Silva M.A.G and Valladares-Neto J},

url = {https://pubmed.ncbi.nlm.nih.gov/34546287/},

doi = {10.2319/040921-292.1},

year  = {2022},

date = {2022-01-01},

journal = {Angle Orthodontist},

volume = {92},

issue = {1},

pages = {118-126},

abstract = {Objectives: To analyze the prevalence of mandibular asymmetry in skeletal sagittal malocclusions.



Materials and methods: PubMed/MEDLINE, EMBASE, LILACS, Web of Science, Scopus, LIVIVO and gray literature (OpenGrey, ProQuest, and Google Scholar) were electronically searched. Two independent investigators selected the eligible studies, and assessed risk of bias and certainty of evidence (GRADE). One reviewer independently extracted the data and the second reviewer checked this information. Any disagreement between the reviewers in each phase was resolved by discussion between them and/or involved a third reviewer for final decision.



Results: Electronic search identified 5,132 studies, and 5 observational studies were included. Risk of bias was low in two studies, moderate in one, and high in two. The studies showed high heterogeneity. Mandibular asymmetry ranged from 17.43% to 72.95% in overall samples. Horizontal chin deviation showed a prevalence of 17.66% to 55.6% asymmetry in Class I malocclusions, and 68.98% in vertical asymmetry index. In Class II patients, prevalence of mandibular asymmetry varied from 10% to 25.5% in horizontal chin deviation, and 71.7% in vertical asymmetry index. The Class III sample showed a prevalence of mandibular asymmetry ranging from 22.93% to 78% in horizontal chin deviation and 80.4% in vertical asymmetry index. Patients seeking orthodontic or orthognathic surgery treatment showed greater prevalence of mandibular asymmetry.



Conclusions: Skeletal Class III malocclusion showed the greatest prevalence of mandibular asymmetry. Mandibular vertical asymmetry showed a marked prevalence in all malocclusions. However, conclusions should be interpreted with caution due to use of convenience samples and low-quality study outcomes.},

keywords = {Angle's malocclusion classification, asymmetry, mandible, prevalence, systematic review},

pubstate = {published},

tppubtype = {article}

}

@article{Linares2018,

title = {Mandible and skull segmentation in cone-beam computed tomography using super-voxels and graph clustering},

author = {C L Oscar and B Jonas and R Dirceu and B N Joao and H Bernd },

url = {https://link.springer.com/article/10.1007/s00371-018-1511-0},

doi = {https://doi.org/10.1007/s00371-018-1511-0},

year  = {2018},

date = {2018-04-26},

urldate = {2018-04-26},

journal = {The Visual Computer},

volume = {35},

pages = {1461-1474},

abstract = {Cone beam computed tomography (CBCT) is a medical imaging technique employed for diagnosis and treatment of patients with cranio-maxillofacial deformities. CBCT 3D reconstruction and segmentation of bones such as mandible or maxilla are essential procedures in surgical and orthodontic treatments. However, CBCT image processing may be impaired by features such as low contrast, inhomogeneity, noise and artifacts. Besides, values assigned to voxels are relative Hounsfield units unlike traditional computed tomography (CT). Such drawbacks render CBCT segmentation a difficult and time-consuming task, usually performed manually with tools designed for medical image processing. We present an interactive two-stage method for the segmentation of CBCT: (i) we first perform an automatic segmentation of bone structures with super-voxels, allowing a compact graph representation of the 3D data; (ii) next, a user-placed seed process guides a graph partitioning algorithm, splitting the extracted bones into mandible and skull. We have evaluated our segmentation method in three different scenarios and compared the results with ground truth data of the mandible and the skull. Results show that our method produces accurate segmentation and is robust to changes in parameters. We also compared our method with two similar segmentation strategy and showed that it produces more accurate segmentation. Finally, we evaluated our method for CT data of patients with deformed or missing bones and the segmentation was accurate for all data. The segmentation of a typical CBCT takes in average 5 min, which is faster than most techniques currently available.},

keywords = {bone segmentation, Cone-beam computed tomography, graph clustering, mandible, skull, super-voxels},

pubstate = {published},

tppubtype = {article}

}