Dataset Information

Gingival shape analysis using surface curvature estimation of the intraoral scans.

ABSTRACT:

Background

Despite many advances in dentistry, no objective and quantitative method is available to evaluate gingival shape. The surface curvature of the optical scans represents an unexploited possibility. The present study aimed to test surface curvature estimation of intraoral scans for objective evaluation of gingival shape.

Methods

The method consists of four main steps, i.e., optical scanning, surface curvature estimation, region of interest (ROI) definition, and gingival shape analysis. Six different curvature measures and three different diameters were tested for surface curvature estimation on central (n = 78) and interdental ROI (n = 88) of patients with advanced periodontitis to quantify gingiva with a novel gingival shape parameter (GS). The reproducibility was evaluated by repeating the method on two consecutive intraoral scans obtained with a scan-rescan process of the same patient at the same time point (n = 8).

Results

Minimum and mean curvature measures computed at 2 mm diameter seem optimal GS to quantify shape at central and interdental ROI, respectively. The mean (and standard deviation) of the GS was 0.33 ± 0.07 and 0.19 ± 0.09 for central ROI using minimum, and interdental ROI using mean curvature measure, respectively, computed at a diameter of 2 mm. The method's reproducibility evaluated on scan-rescan models for the above-mentioned ROI and curvature measures was 0.02 and 0.01, respectively.

Conclusions

Surface curvature estimation of the intraoral optical scans presents a precise and highly reproducible method for the objective gingival shape quantification enabling the detection of subtle changes. A careful selection of parameters for surface curvature estimation and curvature measures is required.

SUBMITTER: Kuralt M

PROVIDER: S-EPMC9275066 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Gingival shape analysis using surface curvature estimation of the intraoral scans.

Kuralt Marko M Cmok Kučič Alja A Gašperšič Rok R Grošelj Jan J Knez Marjeta M Fidler Aleš A

BMC oral health 20220712 1

<h4>Background</h4>Despite many advances in dentistry, no objective and quantitative method is available to evaluate gingival shape. The surface curvature of the optical scans represents an unexploited possibility. The present study aimed to test surface curvature estimation of intraoral scans for objective evaluation of gingival shape.<h4>Methods</h4>The method consists of four main steps, i.e., optical scanning, surface curvature estimation, region of interest (ROI) definition, and gingival sh ...[more]

PMID: 35820843

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Project description:PurposeThis work concerns computed tomography (CT)-based cardiac functional analysis (CFA) with a reduced radiation dose. As CT-CFA requires images over the entire heartbeat, the scans are often performed at 10-20% of the tube current settings that are typically used for coronary CT angiography. A large image noise then degrades the accuracy of motion estimation. Moreover, even if the scan was performed during the sinus rhythm, the cardiac motion observed in CT images may not be cyclic with patients with atrial fibrillation. In this study, we propose to use two CT scan data, one for CT angiography at a quiescent phase at a standard dose and the other for CFA over the entire heart beat at a lower dose.MethodsWe have made the following four modifications to an image-based cardiac motion estimation method we have previously developed for a full-dose retrospectively gated coronary CT angiography: (a) a full-dose prospectively gated coronary CT angiography image acquired at the least motion phase was used as the reference image; (b) a three-dimensional median filter was applied to lower-dose retrospectively gated cardiac images acquired at 20 phases over one heartbeat in order to reduce image noise; (c) the strength of the temporal regularization term was made adaptive; and (d) a one-dimensional temporal filter was applied to the estimated motion vector field in order to decrease jaggy motion patterns. We describe the conventional method iME1 and the proposed method iME2 in this article. Five observers assessed the accuracy of the estimated motion vector field of iME2 and iME1 using a 4-point scale. The observers repeated the assessment with data presented in a new random order 1 week after the first assessment session.ResultsThe study confirmed that the proposed iME2 was robust against the mismatch of noise levels, contrast enhancement levels, and shapes of the chambers. There was a statistically significant difference between iME2 and iME1 (accuracy score, 2.08 ± 0.81 versus 2.77 ± 0.98, P < 0.01) and the improvement by the score of + 0.69 seemed clinically relevant. Inter-observer concordance was good: The inter-class correlation coefficient was 0.63 and Kendall's rank correlation coefficients were in the range of 0.41-0.67 (P < 0.01), respectively. Intra-observer reproducibility between sessions was good with the inter-class correlation coefficient of 0.76.ConclusionWe have proposed iME2 method for CT-CFA with two CT scans. The observer study verified the robustness and accuracy of iME2 method and its improved performance over iME1 method.

Dataset Information

Gingival shape analysis using surface curvature estimation of the intraoral scans.

Background

Methods

Results

Conclusions

Publications

Gingival shape analysis using surface curvature estimation of the intraoral scans.

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