Predictability of bone density at posterior mandibular implant sites using cone-beam computed tomography intensity values

Alkhader, Mustafa; Hudieb, Malik; Khader, Yousef

doi:10.4103/ejd.ejd_14_17

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…Later, using visualization technology, the medical image sequence is transformed into a three-dimensional space model and the slice data are visually displayed to the medical sta . It can quickly and accurately perform multidirectional and multilevel observation of the patient's slice data, which greatly reduces the clinical cost and improves the treatment e ciency [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Technology Based on Attention Mechanism and ENet

Ling

Hou

Gong

2022

Computational Intelligence and Neuroscience

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With the development of today’s society, medical technology is becoming more and more important in people’s daily diagnosis and treatment and the number of computed tomography (CT) images and MRI images is also increasing. It is difficult to meet today’s needs for segmentation and recognition of medical images by manpower alone. Therefore, the use of computer technology for automatic segmentation has received extensive attention from researchers. We design a tooth CT image segmentation method combining attention mechanism and ENet. First, dilated convolution is used with the spatial information path, with a small downsampling factor to preserve the resolution of the image. Second, an attention mechanism is added to the segmentation network based on CT image features to improve the accuracy of segmentation. Then, the designed feature fusion module obtains the segmentation result of the tooth CT image. It was verified on tooth CT image dataset published by West China Hospital, and the average intersection ratio and accuracy were used as the metric. The results show that, on the dataset of West China Hospital, Mean Intersection over Union (MIOU) and accuracy are 83.47% and 95.28%, respectively, which are 3.3% and 8.09% higher than the traditional model. Compared with the multiple watershed algorithm, the Chan–Vese segmentation algorithm, and the graph cut segmentation algorithm, our algorithm increases the calculation time by 56.52%, 91.52%, and 62.96%, respectively. It can be seen that our algorithm has obvious advantages in MIOU, accuracy, and calculation time.

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Section: Introductionmentioning

confidence: 99%

Image Segmentation Technology Based on Attention Mechanism and ENet

Ling

Hou

Gong

2022

Computational Intelligence and Neuroscience

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“…Further, in the study, when we evaluated bone density by two separate observers at posterior mandibular implant sites, it was seen that both intermediate density and high density of bone were commonly found, while low-density type of bone was found to be least in this area. Contrary to this, Alkhader et al 14 reported that almost half of the posterior mandible sites had intermediate density, while the rest had both low-and high-density types of bone. However, both these studies emphasize the importance of site-specific evaluation of bone density before placement of implants.…”

Section: Discussionmentioning

confidence: 88%

“…Some more studies done previously by de Oliveira et al, 11 Norton and Gamble, 12 and Trisi and Rao 13 showed difficulty in the subjective classification of the bone density. Thus, similar to Rebaudi classification, Alkhader et al 14 proposed to classify bone density at implant site into low, intermediate, and high density. In the present study, we also used a similar classification to classify the bone Gender difference was not significant in the studied sample, but still it was observed that the mean bone density of males was 690.5 ± 104.12 HU and that in females, it was 580.20 ± 120.2 HU.…”

Section: Discussionmentioning

confidence: 99%

Qualitative Assessment of Reliability of Cone-beam Computed Tomography in evaluating Bone Density at Posterior Mandibular Implant Site

Dahiya

Kumar

Bajaj

et al. 2018

The Journal of Contemporary Dental Practice

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The success of dental implants depends primarily on the primary implant stability and the bone density so that predictable osseointegration can be achieved. To achieve the desired results, systematic preoperative planning for implant placement is required. The sole aim of the study was to assess the reliability of preoperative bone density of mandibular posterior region for implant placement using computerized tomography-derived bone densities in Hounsfield units (HU). A total of 200 patients with 352 implant sites between 2014 and 2017 were assessed for the posterior mandibular area using cone-beam computed tomography (CBCT). Evaluation was done by two experienced observers independently. The mean bone density of males was 690.5 ± 104.12 HU and in females, it was 580.20 ± 120.2 HU. Overall, 21% of sites were of low bone density, 39.5% were of intermediate density, and 39.4% were of high density. Receiver operating characteristic (ROC) analysis presented that the CBCT intensity values had a high predictive power for predicting both high-density sites and intermediate-density sites. We can say from our results that, for predicting the bone densities in posterior mandible for determining implant sites, so as to achieve best osseointegration, CBCT values can be reliably used. It has been proved that bone density and implant stability are dependent on each other and osseointegration is important for the success of treatment. With advancements in dentistry and introduction of CBCT, treatment planning and prediction of appropriate implant sites could be made easy and more predictable. Thus, we can say that CBCT can be considered an alternative diagnostic tool for the bone density evaluation during treatment planning for implant placement. Bone density, Cone-beam computed tomography, Implants, Osseointegration.

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“…Chenchev IL et al, (36) also reported the beneficial application of plateletrich fibrin and injectable platelet-rich Fibrin in combination of bone substitute material for alveolar ridge augmentation. According to Alkhader M et al, (37) CBCT was very useful for predicting bone density at posterior mandibular implant sites. J Zhang et al, (38) also reported the importance of CBCT in bone density measurements.…”

Section: Discussionmentioning

confidence: 99%

Cortical Tenting Technique for Vertical Augmentation of Atrophic Posterior Part of the Mandible (Clinical Trial)

Hares

Mohamed

Noureldin

2021

Alexandria Dental Journal

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INTRODUCTION: The progressive decrease in the alveolar bone volume after tooth loss impedes the use of dental implants for rehabilitation of edentulous regions. This fact is of utmost significance in the posterior areas of mandible, where the presence of inferior alveolar nerve further complicates the proper insertion of dental implants. Considering the drawbacks of the non-reconstructive treatment options, restoring deficits of alveolar ridges by reconstructive procedures might still be more practicable, despite the invasiveness. The majority of reconstructive techniques involve a process of bone grafting and vertical ridge augmentation. Cortical tenting technique is used for vertical ridge augmentation and can achieve a good degree of success. OBJECTIVES: To evaluate the effectiveness of cortical tenting technique in vertical ridge augmentation of atrophic posterior mandibles. METHODS: The study was made on ten patients with atrophic posterior part of the mandible. In each one of them, the atrophic alveolar ridge will be treated with cortical tenting technique. Bone height over the mandibular canal was measured on Standardized CBCT scans taken preoperatively (base line), and 1 week and 4 months postoperatively and bone density was measured at grafted area 4 months postoperatively. RESULTS: The average final bone gain was 4.540 mm at the end of follow up period with 1.700 mm average of graft resorption. Estimated bone density was measured also at grafted area 4 months after surgery with a mean value of 398.59 voxel value (VV). CONCLUSION: The use of cortical block graft taken from the ramus of the mandible to augment vertical bone defect in the posterior of the mandible has significant success rates.

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Predictability of bone density at posterior mandibular implant sites using cone-beam computed tomography intensity values

Cited by 12 publications

References 20 publications

Image Segmentation Technology Based on Attention Mechanism and ENet

Image Segmentation Technology Based on Attention Mechanism and ENet

Qualitative Assessment of Reliability of Cone-beam Computed Tomography in evaluating Bone Density at Posterior Mandibular Implant Site

Cortical Tenting Technique for Vertical Augmentation of Atrophic Posterior Part of the Mandible (Clinical Trial)

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