Machine learning for identification of dental implant systems based on shape – A descriptive study

Benakatti, Veena; Anandhalli, Mallikarjun

doi:10.4103/jips.jips_324_21

Cited by 18 publications

(23 citation statements)

References 16 publications

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“…A number of studies [ 14 , 17 – 21 ] have applied machine learning and especially deep learning algorithms for the purpose of classifying dental implants and achieved accuracies of 0.63 to 0.96. In the aforementioned systems the classification of a dental implant is based on the type (brand or model) of the dental implant.…”

Section: Discussionmentioning

confidence: 99%

“…Morais et al [ 14 ] and Benakatti et al [ 17 ] employed machine learning-based algorithms for the purpose of classifying dental implants in X-ray images. A k -nearest neighbour (KNN) algorithm is proposed by Morais et al, while Benakatti et al also investigated support vector machines (SVMs), as well as X boost and logistic regression classifiers for the purpose of identifying dental implants achieving an average accuracy of 67%.…”

Section: Related Workmentioning

confidence: 99%

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Deep learning-based dental implant recognition using synthetic X-ray images

Kohlakala

Coetzer

Bertels

et al. 2022

Med Biol Eng Comput

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A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep learning-based dental implant recognition using synthetic X-ray images

Kohlakala

Coetzer

Bertels

et al. 2022

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

“…125 Regarding the capabilities of AI, ML along with logistic regression and support vector machine (SVM) algorithms has a potential to distinguish diverse dental implant systems on radiographs, thus minimizing trialand-error, invasive dental approaches, and chairside time. 126…”

Section: Application Of Ai For Dental Restoration/ Implant Detectionmentioning

confidence: 99%

“…122 In another study, an ML classifier of dental implants using SVM and logistic regression revealed a mean accuracy of 67%. 126 However, a deep CNN system (GoogLeNet Inception-v3), which was designed for detecting three implant systems including Osstem TSIII, Dentium Superline, and Straumann BLT, recorded the highest accuracies for Straumann BLT (99.4% for panoramic radiographic images and 99.5% for periapical radiographic images). 130 Also, this system revealed a diagnostic accuracy of 93.8% when differentiating three different implant brands for six implant models including Nobel Biocare NobelActive and Brånemark System, Straumann Bone Level and Tissue Level, and Zimmer Biomet Dental Tapered Screw-Vent and SwissPlus.…”

Section: Accuracy Of Ai For Dental Restoration/implant Detectionmentioning

confidence: 99%

“…In implant dentistry, some information is needed about the implant system and type, the retention method, and the abutment type for possible dental services and care in the long term 125 . Regarding the capabilities of AI, ML along with logistic regression and support vector machine (SVM) algorithms has a potential to distinguish diverse dental implant systems on radiographs, thus minimizing trial‐and‐error, invasive dental approaches, and chairside time 126 …”

Section: Dental Restoration/implant Detectionmentioning

confidence: 99%

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Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

Tabatabaian

Vora

Mirabbasi

2023

J Esthet Restor Dent

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ObjectiveThe applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination.OverviewAn electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English‐language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed.ConclusionsBased on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice.Clinical Significance: AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.

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Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review

Șalgău,

Morar,

Zgarta

et al. 2024

Ann Biomed Eng

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Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

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Machine learning for identification of dental implant systems based on shape – A descriptive study

Cited by 18 publications

References 16 publications

Deep learning-based dental implant recognition using synthetic X-ray images

Deep learning-based dental implant recognition using synthetic X-ray images

Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review

Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review

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