Clinical retrospective study of dental implant removal: do patients who require implant removal desire implant prosthesis again?

Sukegawa, Shintaro; Saika, Masato; Tamamura, Ryo; Nakano, Keisuke; Takabatake, Kiyofumi; Kawai, Hiroki; Nagatsuka, Hitoshi; Furuki, Yoshihiko

doi:10.4317/medoral.23789

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…Second, another major task was implant classification 23,30,31,44,45,46,47 . The inability to identify the type of implant and, hence, to re‐use the implant for prosthetic purposes has been found the root cause for the removal of about 3% of implants worldwide given 48 . Most developed models showed desirable accuracy in distinguishing dental implant systems and brands 44 .…”

Section: Discussionmentioning

confidence: 99%

“…23,30,31,44,45,46,47 The inability to identify the type of implant and, hence, to re-use the implant for prosthetic purposes has been found the root cause for the removal of about 3% of implants worldwide given. 48 Most developed models showed desirable accuracy in distinguishing dental implant systems and brands. 44 However, the major limitation was the number of compared systems; at most, 12 different types of dental implant systems had been used for training and classification.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning in periodontology and oral implantology: A scoping review

Mohammad‐Rahimi

Motamedian

Pirayesh

et al. 2022

J of Periodontal Research

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Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta‐analysis was performed. The risk of bias was assessed using the QUADAS‐2 tool. We included 47 studies: focusing on imaging data (n = 20) and non‐imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi‐layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep learning in periodontology and oral implantology: A scoping review

Mohammad‐Rahimi

Motamedian

Pirayesh

et al. 2022

J of Periodontal Research

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“…However, due to the continual updates to implant systems and the introduction of numerous new implant types, it becomes challenging for dentists to precisely identify the implant brand. 3 Consequently, there is an increasing demand for the development of diagnostic tools for identifying newly developed implants.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Dental Implant Identification using Deep Learning Leveraging Artificial Data

Sukegawa,

Yoshii,

Hara

et al. 2023

Preprint

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This study aims to evaluate the potential enhancement in implant classification performance achieved by incorporating artificially generated images of commercially available products into a deep learning process of dental implant classification using panoramic X-ray images. To supplement an existing dataset of 7,946 in vivo dental implant images, a three-dimensional scanner was employed to create an implant surface model. Subsequently, it was used to generate two-dimensional X-ray images, which were compiled with original images to create a comprehensive dataset. Image classification of 10 types of implants was performed using ResNet50 under the following dataset conditions: (A) images of implants in vivo, (B) artificial implant images without background adjustments, and (C) implant images with background adjustments, derived from in vivo images. The classification accuracy for the three datasets is as follows: A registered at 0.8888; B, 0.903, and C, 0.9146. Notably, dataset C demonstrated the highest performance and had the most optimal feature distribution. In the context of deep learning classifiers for dental implants using panoramic X-ray images, incorporating artificially generated X-ray images—designed to mirror the appearance of human body implants—proved to be the most beneficial in enhancing the performance of the classification model.

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“…Although various devices are available as removal tools [ 8 ], understanding the type of implant is necessary for easy removal. Unfortunately, it has also been reported that implant removal is required if continuous maintenance is not possible due to difficulty in identifying the implant brand type [ 9 ]. Therefore, it is important to accurately identify the types of dental implants.…”

Section: Introductionmentioning

confidence: 99%

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

et al. 2022

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Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to “Huge” for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.

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Clinical retrospective study of dental implant removal: do patients who require implant removal desire implant prosthesis again?

Cited by 7 publications

References 30 publications

Deep learning in periodontology and oral implantology: A scoping review

Deep learning in periodontology and oral implantology: A scoping review

Optimizing Dental Implant Identification using Deep Learning Leveraging Artificial Data

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

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