Dataset Information

Studying the Optical 3D Accuracy of Intraoral Scans: An In Vitro Study.

ABSTRACT: There are various scanners available in dental practice with various accuracies. The aim of this study was to compare the 3D capturing accuracy of scans obtained from Trios 3 and Dental Wings scanner. A reference mandibular model was printed from FormLab with reference points in three axes (X, Y, and XY and Z). The printed model was scanned 5 times with 3 scans: normal scan by Trios 3 (Trios 3A), high-resolution scan by Trios 3 (Trios 3B), and normal scan by Dental Wings. After scan, the stereolithography (stl) files were generated. Then, the measurements were made from the computer software using Rhinoceros 3D (Rhino, Robert McNeel & Associates for Windows, Washington DC, USA). The measurements made with digital caliper were taken as control. Statistical analysis was done using one-way ANOVA with post hoc using Sheffe (P < 0.01). Trios 3 presented higher accuracy than Dental Wings and high resolution showed better results. The Dental Wings showed less accuracy at the measurements >50?mm of length and >30?mm in width. There was no significant difference (P > 0.05) of control with the Trios 3A and Trios 3B. Similarly, for the measurements in Z-axis, there was no significant difference of control with each scan (Trios 3A, Trios 3B, and Dental Wings). Accuracy of the scan is affected by the length of the scanning area and scanning pattern. It is less recommended to Dental Wings scan >3-unit prosthesis and that crosses the midline.

SUBMITTER: Amornvit P

PROVIDER: S-EPMC7042536 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Studying the Optical 3D Accuracy of Intraoral Scans: An In Vitro Study.

Amornvit Pokpong P Sanohkan Sasiwimol S Peampring Chaimongkon C

Journal of healthcare engineering 20200214

There are various scanners available in dental practice with various accuracies. The aim of this study was to compare the 3D capturing accuracy of scans obtained from Trios 3 and Dental Wings scanner. A reference mandibular model was printed from FormLab with reference points in three axes (X, Y, and XY and Z). The printed model was scanned 5 times with 3 scans: normal scan by Trios 3 (Trios 3A), high-resolution scan by Trios 3 (Trios 3B), and normal scan by Dental Wi ...[more]

PMID: 32148745

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Project description:BackgroundExcess adiposity in children is strongly correlated with obesity-related metabolic disease in adulthood, including diabetes, cardiovascular disease, and 13 types of cancer. Despite the many long-term health risks of childhood obesity, body mass index (BMI) Z-score is typically the only adiposity marker used in pediatric studies and clinical applications. The effects of regional adiposity are not captured in a single scalar measurement, and their effects on short- and long-term metabolic health are largely unknown. However, clinicians and researchers rarely deploy gold-standard methods for measuring compartmental fat such as magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA) on children and adolescents due to cost or radiation concerns. Three-dimensional optical (3DO) scans are relatively inexpensive to obtain and use non-invasive and radiation-free imaging techniques to capture the external surface geometry of a patient's body. This 3D shape contains cues about the body composition that can be learned from a structured correlation between 3D body shape parameters and reference DXA scans obtained on a sample population.Study aimThis study seeks to introduce a radiation-free, automated 3D optical imaging solution for monitoring body shape and composition in children aged 5-17.MethodsWe introduce an automated, linear learning method to predict total and regional body composition of children aged 5-17 from 3DO scans. We collected 145 male and 206 female 3DO scans on children between the ages of 5 and 17 with three scanners from independent manufacturers. We used an automated shape templating method first introduced on an adult population to fit a topologically consistent 60,000 vertex (60 k) mesh to 3DO scans of arbitrary scanning source and mesh topology. We constructed a parameterized body shape space using principal component analysis (PCA) and estimated a regression matrix between the shape parameters and their associated DXA measurements. We automatically fit scans of 30 male and 38 female participants from a held-out test set and predicted 12 body composition measurements.ResultsThe coefficient of determination (R2) between 3DO predicted body composition and DXA measurements was at least 0.85 for all measurements with the exception of visceral fat on 3D scan predictions. Precision error was 1-4 times larger than that of DXA. No predicted variable was significantly different from DXA measurement except for male trunk lean mass.ConclusionOptical imaging can quickly, safely, and inexpensively estimate regional body composition in children aged 5-17. Frequent repeat measurements can be taken to chart changes in body adiposity over time without risk of radiation overexposure.

Project description:BackgroundMany predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods.PurposeWe introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices.MethodsA fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52 kg/m2 . Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model.ResultsWe achieved coefficients of determination (R2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat), and all root-mean-squared errors were below 3.0 kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x.ConclusionsOur method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.

Dataset Information

Studying the Optical 3D Accuracy of Intraoral Scans: An In Vitro Study.

Publications

Studying the Optical 3D Accuracy of Intraoral Scans: An In Vitro Study.

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