Cone-beam computed tomography artifacts in the presence of dental implants and associated factors: an integrative review

Terrabuio, Bianca Rodrigues; Carvalho, Caroline Gomes; Peralta-Mamani, Mariela; Santos, Paulo Sérgio da Silva; Rubira-Bullen, Izabel Regina Fischer; Rubira, Cássia Maria Fischer

doi:10.5624/isd.20200320

Cited by 16 publications

(19 citation statements)

References 39 publications

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“…The ideal implant fixture classification by deep learning should classify all kinds of implant fixtures available in the market, although hundreds of systems exist. 4 This is the major limitation of this study and other studies with the same purpose because the largest number of implant systems evaluated in a single study was 11. 10 Another limitation is that it did not include a testing process with external data.…”

Section: Discussionmentioning

confidence: 89%

“… 1 As implant treatment has grown more popular, 2 more manufacturers have produced various implant systems. 3 4 5 6 This diversity provides dentists with more options among implant systems. However, since each manufacturer produces implant systems with unique implant parts, 3 5 6 if patient records containing the exact name of the implant system are not available, it can be quite challenging to identify the implant system.…”

Section: Introductionmentioning

confidence: 99%

“… 7 However, without extensive experience in implant treatment using various systems, it is almost impossible to differentiate among systems based on imaging results, 8 and sometimes dentists end up trying to apply every kind of implant part that would fit. To resolve this laborious situation, websites 4 and programs 6 have been developed to facilitate identification based on several characteristics of implant fixtures.…”

Section: Introductionmentioning

confidence: 99%

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Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Kim

et al. 2022

Imaging Sci Dent

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Purpose This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III (Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant (Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/ ), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Kim

et al. 2022

Imaging Sci Dent

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“…Artifacts can be minimized in protocols with smaller FOVs, larger voxel size, and higher kVp, improving image quality. 33 Because the CBCT examinations in the present study had already been performed together with their respective reports, it was not possible to adequately control these factors. This should be considered in future studies to avoid the presence of artifacts and possible diagnostic errors.…”

Section: Discussionmentioning

confidence: 93%

Prevalence of dental implant positioning errors: A cross-sectional study

2022

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Purpose This study evaluated the prevalence of dental implant positioning errors and the most frequently affected oral regions. Materials and Methods A sample was obtained of CBCT images of 590 dental implants from 230 individuals who underwent diagnosis at a radiology center using cone-beam computed tomography from 2017 to 2020. The following variables were considered: thread exposure, violation of the minimum distance between 2 adjacent implants and between the implant and tooth, and implant contact with anatomical structures. Descriptive data analysis and the Pearson chi-square test ( P <0.05) were performed to compare findings according to mouth regions. Results Most (74.4%) of the 590 implants were poorly positioned, with the posterior region of the maxilla being the region most frequently affected by errors. Among the variables analyzed, the most prevalent was thread exposure (54.7%), followed by implant contact with anatomical structures, violation of the recommended distance between 2 implants and violation of the recommended distance between the implant and teeth. Thread exposure was significantly associated with the anterior region of the mandible ( P <0.05). The anterior region of the maxilla was associated with violation of the recommended tooth-implant distance ( P <0.05) and the recommended distance between 2 adjacent implants ( P <0.05). Implant contact with anatomical structures was significantly more likely to occur in the posterior region of the maxilla ( P <0.05). Conclusion Many implants were poorly positioned in the posterior region of the maxilla. Thread exposure was particularly frequent and was significantly associated with the anterior region of the mandible.

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“…As a 3D imaging technique, cone-beam computed tomography (CBCT) is the modality of choice for various purposes in dentistry including pre- and post-operative imaging of dental implants 17 , 18 . CBCT provides volumetric images with reliable details; however, beam hardening artifacts that are produced by high density objects such as titanium implants could degrade image quality to a point that diagnostic tasks including fracture diagnosis become almost impossible 19 – 21 .…”

Section: Introductionmentioning

confidence: 99%

A diagnostic accuracy study on an innovative auto-edge detection technique for identifying simulated implant fractures on radiographic images

Khosravifard

Saberi

Khosravifard

et al. 2022

Sci Rep

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Implant fracture is a rare but devastating complication of treatment in partially or fully edentulous patients which requires prompt diagnosis. Nevertheless, studies on defining the most accurate technique for the detection of implant fractures are lacking. In the present study, the Canny edge detection algorithm was applied on multiple radiographic modalities including parallel periapical (PPA), oblique periapical (OPA), and cone beam CT (CBCT) with and without metal artifact reduction (MAR) to examine its accuracy for diagnosis of simulated implant fractures. Radiographs were taken from 24 intact implants and 24 implants with artificially created fractures. Images were evaluated in their original and Canny formats. The accuracy of each radiograph was assessed by comparison with a reference standard of direct observation of the implant. The greatest area under the receiver operating characteristic curve belonged to Canny CBCT with MAR (0.958), followed by original CBCT with MAR (0.917), original CBCT without MAR = Canny CBCT without MAR = Canny OPA (0.875), Canny PPA (0.833), original PPA = original OPA (0.792), respectively. The Canny edge detection algorithm is suggested as an innovative method for accurate diagnosis of clinically suspected implant fractures on CBCT and periapical radiographies.

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Cone-beam computed tomography artifacts in the presence of dental implants and associated factors: an integrative review

Cited by 16 publications

References 39 publications

Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study

Prevalence of dental implant positioning errors: A cross-sectional study

A diagnostic accuracy study on an innovative auto-edge detection technique for identifying simulated implant fractures on radiographic images

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