scholarly journals New Classification of Crown Forms and Gingival Characteristics in Taiwanese

2008 ◽  
Vol 2 (1) ◽  
pp. 114-119 ◽  
Author(s):  
Yu-Hsiang Chou ◽  
Chi-Cheng Tsai ◽  
Jen-Chyan Wang ◽  
Ya-Ping Ho ◽  
Kun-Yen Ho ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Anterior Tooth ◽  
New Classification ◽  
Gingival Thickness ◽  
Crown Width ◽  
Implant Placement ◽  
Compound C ◽  
Significant Difference ◽  
Interdental Papilla

Objectives: The aim of the investigation was to examine the forms of the crowns in the maxillary anterior tooth segment and corresponding gingival characteristics among healthy Taiwanese subjects. Materials and Methods: The crown width at the apical third (CW), length (CL), gingival angle (GA) and the interdental papilla height were assessed from the diagnostic stone model using a calibrated periodontal caliper. A CW/CL-ratio was calculated for each tooth and averaged for each tooth region. Gingival thickness (GT) and width of keratinized gingiva (WG) were measured clinically. Results: The cluster analysis revealed 3 classifications of crown forms: narrow (N), compound (C) and square (S) types. There was a significant difference among the 3 classifications with respect to CW/CL-ratio, GT, and WG (p < 0.0001). Conclusions: The results demonstrated varied crown forms and corresponding gingival characteristics in Caucasian and Taiwanese. The new classifications hinted that there was a polymorphism in different races and could be a valuable esthetic guideline and reference for anterior tooth rehabilitation, including various periodontal and restorative treatments and anterior implant placement procedures in Taiwanese.

Accuracy of high-resolution ultrasound (US) for gingival soft tissue thickness mesurement in edentulous patients prior to implant placement

2020 ◽  
pp. 20200309
Author(s):  
Gül Sönmez ◽  
Kıvanç Kamburoğlu ◽  
Ayşe Gülşahı
Keyword(s):  
High Resolution ◽  
Soft Tissue ◽  
Alveolar Process ◽  
Tissue Thickness ◽  
Gingival Thickness ◽  
Implant Placement ◽  
Soft Tissue Thickness ◽  
Significant Difference ◽  
Edentulous Patients ◽  
Clinical Measurements

Objectives: To evaluate and compare the accuracy of high-resolution ultrasound (US) with two different cone beam CT (CBCT) units and clinical assessment for measuring gingival soft tissue thickness in edentulous patients prior to implant placement. Methods and materials: The study consisted of 40 maxillary implant sites of 40 healthy patients (20 females, 20 males; mean age, 47.88 years). We prospectively evaluated labial/buccal gingival thickness in 40 implant regions (16 anterior and 24 posterior) by using limited field of view (FOV) CBCT images and US images in comparison to gold standard transgingival probing measurements. One-way analysis of variance (ANOVA) was used to compare mean measurements obtained from CBCT (Morita and Planmeca), US, and transgingival probing. Interclass correlation coefficient (ICC) estimates were calculated based on means with two-way mixed and absolute-agreement model. Bland Altman plot was used to describe agreement between clinical vs US and CBCT measurements by constructing limits of agreement. Statistical significance was set at p < 0.05. Results: There was no significant difference between methods used according to mean gingival thickness measurements obtained from the top (p = 0.519) and bottom (p = 0.346) of the alveolar process. US and CBCT measurements highly correlated with clinical measurements for both top and bottom alveolar process gingival thickness (p < 0.001). Distribution of differences between clinical measurements and both CBCT measurements showed statistically significant differences according to 0 (p < 0.05). Distribution of differences between clinical measurements and US measurements did not show statistically significant difference (p > 0.05). Conclusion: High-resolution US provided accurate information for the measurement of gingival soft tissue thickness in edentulous patients prior to implant placement.

scholarly journals Analysis of the mandibular canal course using unsupervised machine learning algorithm

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260194
Author(s):  
Young Hyun Kim ◽  
Kug Jin Jeon ◽  
Chena Lee ◽  
Yoon Joo Choi ◽  
Hoi-In Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Learning Algorithm ◽  
Mandibular Canal ◽  
Unsupervised Machine Learning ◽  
Computed Tomography Images ◽  
Significant Difference ◽  
Cluster 2 ◽  
Axial View

Objectives Anatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population. Materials and methods A total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis. Results Cluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference. Conclusion The MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.

scholarly journals CLASSIFICATION OF FINANCIAL MECHANISMS OF HEALTHCARE SYSTEMS IN THE COUNTRIES OF EUROPEAN UNION

2020 ◽  
Author(s):  
Nadiya Dubrovina ◽  
◽  
Stanislav Filip ◽  
Vira Dubrovina ◽  
◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Healthcare System ◽  
Quantitative Methods ◽  
System Analysis ◽  
Healthcare Systems ◽  
New Classification ◽  
Eu Countries ◽  
The Eu

In the article the problems of the classification of financial mechanisms of healthcare systems were considered. The well-known types of the financial mechanisms of healthcare systems were described and the authors argued the importance of the development of new classification of financial mechanism of healthcare systems in the EU, which will be based on the application of quantitative methods. The purpose of the research is cluster (or regimes of funding healthcare system) analysis and development for EU countries over long-term period of 2000-2017.The authors used k-means method for the cluster analysis and revealed 5 clusters (groups) for the characteristics of the different regimes of funding healthcare system in the EU countries.

scholarly journals A New Classification of Diabetic Gait Pattern Based on Cluster Analysis of Biomechanical Data

2010 ◽  
Vol 4 (5) ◽  
pp. 1127-1138 ◽  
Author(s):  
Zimi Sawacha ◽  
Gabriella Guarneri ◽  
Angelo Avogaro ◽  
Claudio Cobelli
Keyword(s):  
Cluster Analysis ◽  
Gait Pattern ◽  
New Classification

Neurophysiologic classification of Wilson's disease by using cluster analysis

2006 ◽  
Vol 37 (01) ◽  
Author(s):  
W Hermann ◽  
T Villmann ◽  
HJ Kühn ◽  
P Baum ◽  
G Reichel ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Wilson’S Disease ◽  
Wilson's Disease

New Classification of Gemstone Colours

1971 ◽  
Vol 12 (7) ◽  
pp. 262-266
Author(s):  
K. F. Chudoba
Keyword(s):  
New Classification

Is perioperative antibiotic necessary in straightforward implant placement procedures?

2020 ◽  
Author(s):  
Elçin Bedeloğlu ◽  
Mustafa Yalçın ◽  
Cenker Zeki Koyuncuoğlu
Keyword(s):  
Clavulanic Acid ◽  
Dental Implant ◽  
Prophylactic Antibiotic ◽  
Antibiotic Use ◽  
Implant Failure ◽  
Control Group ◽  
Implant Placement ◽  
Significant Difference ◽  
Amoxicillin Clavulanic Acid ◽  
Experimental Group

The purpose of this non-random retrospective cohort study was to evaluate the impact of prophylactic antibiotic on early outcomes including postoperative pain, swelling, bleeding and cyanosis in patients undergoing dental implant placement before prosthetic loading. Seventy-five patients (45 males, 30 females) whose dental implant placement were completed, included to the study. Patients used prophylactic antibiotics were defined as the experimental group and those who did not, were defined as the control group. The experimental group received 2 g amoxicillin + clavulanic acid 1 h preoperatively and 1 g amoxicillin + clavulanic acid twice a day for 5 days postoperatively while the control group had received no prophylactic antibiotic therapy perioperatively. Data on pain, swelling, bleeding, cyanosis, flap dehiscence, suppuration and implant failure were analyzed on postoperative days 2, 7, and 14 and week 12. No statistically significant difference was detected between the two groups with regard to pain and swelling on postoperative days 2, 7, and 14 and week 12 ( p &gt;0.05), while the severity of pain and swelling were greater on day 2 compared to day 7 and 14 and week 12 in both groups ( p =0.001 and p &lt;0.05, respectively). Similarly, no significant difference was found between the two groups with regard to postoperative bleeding and cyanosis. Although flap dehiscence was more severe on day 7 in the experimental group, no significant difference was found between the two groups with regard to the percentage of flap dehiscence assessed at other time points. Within limitations of the study, it has been demonstrated that antibiotic use has no effect on implant failure rates in dental implant surgery with a limited number of implants. We conclude that perioperative antibiotic use may not be required in straightforward implant placement procedures. Further randomized control clinical studies with higher numbers of patients and implants are needed to substantiate our findings.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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