Three-dimensional assessment of facial asymmetry: A systematic review

Akhil, Gopi; Kumar, Kullampalayam Palanisamy Senthil; Raja, Subramani; Janardhanan, K

doi:10.4103/0975-7406.163491

Cited by 29 publications

(23 citation statements)

References 35 publications

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“…(Proffit et al, 1991) However, no data was available on the predictable factors for the need of overcorrection. Even though facial analyses were performed pre-and post-operative, specificity and sensitivity of these measurements may be questioned since there was no use of modern measurements (Akhil et al, 2015) such as 3D analysis.…”

Section: Resultsmentioning

confidence: 99%

Surgical correction of the midface in craniofacial microsomia. Part 1: A systematic review

Lande

Pluijmers

Caron

et al. 2018

Journal of Cranio-Maxillofacial Surgery

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Section: Resultsmentioning

confidence: 99%

Surgical correction of the midface in craniofacial microsomia. Part 1: A systematic review

Lande

Pluijmers

Caron

et al. 2018

Journal of Cranio-Maxillofacial Surgery

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“…Present technology has integrated 3D volumetric and two-dimensional linear measurements, allowing for assessment of craniofacial growth and orthodontic diagnosis and treatment. 16 Future analysis will use a larger data set with extensive space analysis in three dimensions to assess maxillary growth. As a fundamental step in image data processing, identifying and delineating the anatomical structures of interest, known as image segmentation, has become a bottleneck step in the clinical application of 3D CBCT image analysis.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in Orthodontics: Introducing a 3d Auto-segmentation and Auto-landmark Finder of Cbct Images To Assess Maxillary Constriction in Unilateral Impacted Canine patients

Chen

Wang

et al. 2019

The Angle Orthodontist

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Objectives: To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information. Materials and Methods: A machine learning algorithm utilizing Learning-based multi-source IntegratioN frameworK for Segmentation (LINKS) was used with cone-beam computed tomography (CBCT) images to quantify volumetric skeletal maxilla discrepancies of 30 study group (SG) patients with unilaterally impacted maxillary canines and 30 healthy control group (CG) subjects. Fully automatic segmentation was implemented for maxilla isolation, and maxillary volumetric and linear measurements were performed. Analysis of variance was used for statistical evaluation. Results: Maxillary structure was successfully auto-segmented, with an average dice ratio of 0.80 for three-dimensional image segmentations and a minimal mean difference of two voxels on the midsagittal plane for digitized landmarks between the manually identified and the machine learning–based (LINKS) methods. No significant difference in bone volume was found between impaction ([2.37 ± 0.34] 104 mm3) and nonimpaction ([2.36 ± 0.35] 104 mm3) sides of SG. The SG maxillae had significantly smaller volumes, widths, heights, and depths (P < .05) than CG. Conclusions: The data suggest that palatal expansion could be beneficial for those with unilateral canine impaction, as underdevelopment of the maxilla often accompanies that condition in the early teen years. Fast and efficient CBCT image segmentation will allow large clinical data sets to be analyzed effectively.

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“…Surgical correction of mandibular asymmetry is challenging in the field of orthognathic surgery. The aberrant shape as well as the malposition of the mandible results in multiplanar facial asymmetries …”

Section: Introductionmentioning

confidence: 99%

Median Lingual Foramen, a new midmandibular cephalometric landmark

Vandekerckhove

Deibel

Vinayahalingam

et al. 2020

Orthod Craniofacial Res

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Surgical correction of mandibular asymmetry is challenging in the field of orthognathic surgery. The aberrant shape as well as the malposition of the mandible results in multiplanar facial asymmetries. 1 The emergence of three-dimensional (3D) cone beam computed tomography (CBCT) has enabled orthognathic surgeons to diagnose maxillofacial deformities and to allow a more accurate pre-operative planning. 1-6 Whilst planning an operation, cephalometric landmarks are used to determine the morphology of the facial skeleton. The traditional landmarks to indicate the midline of the mandible include pogonion, mention and genial tubercle. The reliability and reproducibility of conventional cephalometric landmarks were validated on patients without a clinically significant asymmetry of the mandible. 3,7-10 In asymmetrical cases, the landmarks are more difficult to identify. Mirroring is frequently used to superimpose one hemimandible upon the other in order to analyse the severity of mandibular asymmetries and calculate the difference in volume. It provides accurate

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Three-dimensional assessment of facial asymmetry: A systematic review

Cited by 29 publications

References 35 publications

Surgical correction of the midface in craniofacial microsomia. Part 1: A systematic review

Surgical correction of the midface in craniofacial microsomia. Part 1: A systematic review

Machine Learning in Orthodontics: Introducing a 3d Auto-segmentation and Auto-landmark Finder of Cbct Images To Assess Maxillary Constriction in Unilateral Impacted Canine patients

Median Lingual Foramen, a new midmandibular cephalometric landmark

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