Hierarchical CNN-based occlusal surface morphology analysis for classifying posterior tooth type using augmented images from 3D dental surface models

Chen, Qingguang; Huang, Junchao; Sher, Hassan; Zhu, Haihua; Lian, Luya; Lai, Xiaomin; Wei, Kaihua

doi:10.1016/j.cmpb.2021.106295

Cited by 13 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For detailed information on DLbased medical image classification methods in clinical applications, two excellent reviews by Litjens and Ker [39,40] are recommended. CNNs have also found applications in dentistry for classifying medical images [41][42][43].…”

Section: Classificationmentioning

confidence: 99%

“…− Poor-quality images, such as those with overlapping teeth or distorted tooth length, can mislead the diagnosis, and the model does not automatically exclude them [42,77]. − Capturing various aspects of a tooth and the difficulties associated with internal tooth structure and the interproximal tooth surface in intraoral photography [43,56,66,102]. − Handling Incorrectly Oriented Radiographs [56].…”

Section: Image Qualitymentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review

Brahmi,

Jdey,

Alimi

2024

Preprint

View full text Add to dashboard Cite

In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Results: In incorporating 45 studies, we identified selection criteria and research objectives, addressing significant gaps in the existing literature. These studies assist clinicians in examining dental conditions and classifying dental structures, including caries detection and the identification of various tooth types. We evaluated model performance, addressing the identified gaps, using diverse metrics that we strive to list and explain. Conclusion:This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.

show abstract

Section: Classificationmentioning

confidence: 99%

Section: Image Qualitymentioning

confidence: 99%

Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review

Brahmi,

Jdey,

Alimi

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…We may design our network on a 3D region proposal network (RPN) with a novel learned-similarity matrix in the second stage, which enables us to train quickly, efficiently remove redundant proposals, and store GPU memory. In Chen et al (2021), an occlusal surface morphology study and CNN are used to evaluate an eight-class posterior tooth type classifier. A depth picture and a straightforward CNN-based classifier have been used in place of the occlusal surface in 3D.…”

Section: Related Workmentioning

confidence: 99%

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

Awari

Neelakandan²,

Janagaraj³

et al. 2022

Expert Systems

View full text Add to dashboard Cite

Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.

show abstract

“…This includes such research methods as visual examination [48], geometric morphometric analysis [49,50], measurements [51], or research and education [52]. In general, digital imaging have significantly provided for research and there are numerous cases of their successful application and development of new directions for research [53][54][55][56][57].…”

Section: Imaging Techniquesmentioning

confidence: 99%

New Approach to Dental Morphometric Research Based on 3D Imaging Techniques

2021

View full text Add to dashboard Cite

Recent progress in imaging and image processing techniques has provided for improvements in odontological research in a variety of aspects. Thus, the presented method has been developed precisely in order to assess metrically 3D reconstructions of teeth. Rapidly and accurately obtained data of a wide range and appropriate density are sufficient enough for morphometric studies rather than tooth size assessments which are inherent to conventional techniques. The main contributions providing for holistic and objective morphometric analysis of teeth are the following: (1) interpretation of basic dental morphological features; (2) automated of orientational coordinate system setup based on tooth surface analysis; (3) new tooth morphometric parameters which could not be obtained through application of conventional odontometric techniques; (4) methodological novelty for automated odontomorphometric analysis pipeline. Application of tomographic imaging, which has been used for obtaining 3D models, expands the proposed method potential further through providing detailed and comprehensive reconstructions of teeth. The current study was conducted on unique material from the archaeological site of Sunghir related to the Upper Palaeolithic period. Metric assessments of external and internal morphological layers of teeth were performed in common orientation and sectioning. The proposed technique allowed more profound analysis of Sunghirian teeth which date back to the times of modern human morphology formation.

show abstract

Hierarchical CNN-based occlusal surface morphology analysis for classifying posterior tooth type using augmented images from 3D dental surface models

Cited by 13 publications

References 28 publications

Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review

Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review

Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

New Approach to Dental Morphometric Research Based on 3D Imaging Techniques

Contact Info

Product

Resources

About