Dataset Information

Three dimensional (3D) imaging techniques in orthodontics-An update.

ABSTRACT: 3D imaging is a technique which develops or creates the impression of depth within an image by deploying 2D data into 3-dimensional format. To aid in quality regulating processes for industrial purposes, 3D imaging has become an extremely valuable factor. Owing to their various drawbacks, a wide range of investigative methods formulated for demonstration of facial structures and the dentition were dilapidated. Currently in medicine, the most prevalent method is perhaps 3D imaging technique renders thorough and problem specific information regarding hard and the soft tissues, such as Computerized Tomography (CT), Cone Beam Computerized Tomography (CBCT), Micro Computerized Tomography (MCT), 3D laser scanning, structured light technique, stereophotogrammetry or 3D surface imaging systems (3dMD), 3D facial morphometry (3DFM), Tuned Aperture Computed Tomography (TACT), and Magnetic Resonance Imaging (MRI). 3D imaging techniques in orthodontics plays an important role by facilitating more elaborated diagnostic information on the precise cases like patients having craniofacial anomalies. Hence, the aim of this study was to review advances in 3D imaging with in the field of orthodontics.

SUBMITTER: Alshammery FA

PROVIDER: S-EPMC7491840 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Three dimensional (3D) imaging techniques in orthodontics-An update.

Alshammery Fahad Abdullah FA

Journal of family medicine and primary care 20200630 6

3D imaging is a technique which develops or creates the impression of depth within an image by deploying 2D data into 3-dimensional format. To aid in quality regulating processes for industrial purposes, 3D imaging has become an extremely valuable factor. Owing to their various drawbacks, a wide range of investigative methods formulated for demonstration of facial structures and the dentition were dilapidated. Currently in medicine, the most prevalent method is perhaps 3D imaging technique rende ...[more]

PMID: 32984098

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Project description:Objective: Facial changes are common among nearly all acromegalic patients. As they develop slowly, patients often fail to notice such changes before they become obvious. Consequently, diagnosis and treatment are often delayed. So far, convenient and accurate early detection of this disease is still unavailable. This study is designed to combine the use of 3D imaging and machine learning techniques in facial feature analysis and identification of acromegalic patients, in an effort to ascertain how both techniques performed in terms of applicability and value in the early detection of the disease. Methods: One hundred and twenty-four participants including 62 patients with acromegaly and 62 matched controls were enrolled. Using three-dimensional imaging techniques, 58 facial parameters were measured on each face. A two-way analysis of variance (ANOVA) and a post-hoc t-tests were conducted to examine the variations of these parameters with disease status and gender. Using linear discriminant analysis (LDA), we further distinguished patients from controls, characterized what combinations of the parameters could best predict disease state and their relative contributions. Results: Patients are significantly different from normal subjects in many variables, and facial changes of male patients are more significant than female ones. Both male and female patients present following major changes: the increase of facial length and breadth, the widening and elevation of the nose, the thickening of vermilion and the enlargement of the mandible. Facial variables which strongly related to the pathological states can be used to predict the morbid state with high accuracy (prediction accuracies 92.86% in females, p < 0.0001 and 75% in males, p < 0.001). We have further testified that only a few variables play a vital role in disease prediction and the vital combination of variables vary with gender. Conclusions: Three-dimensional imaging enables comprehensive and accurate quantification of facial characteristics, which makes it a promising technique to investigate facial features of acromegalic patients. In combination with machine learning technique, patients can be accurately identified and predicted by their facial variables. This approach might be beneficial for the early detection of acromegalic patients and timely consultation to improve their outcomes.

Dataset Information

Three dimensional (3D) imaging techniques in orthodontics-An update.

Publications

Three dimensional (3D) imaging techniques in orthodontics-An update.

Similar Datasets

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