Dental Impression Tray Selection From Maxillary Arch Images Using Multi-Feature Fusion and Ensemble Classifier

Hasan, Muhammad Asif; Abdullah, Norli Anida; Rahman, Mohammad Mustaneer; Idris, Mohd Yamani Idna; Tawfiq, Omar

doi:10.1109/access.2021.3059785

Cited by 7 publications

(4 citation statements)

References 57 publications

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“…In order to solve the selection problem of visual regions and time series, the attention model is constructed; that is, in the deep reinforcement learning network, a small sampling area is used as the model input, and the next vision is based on the time series information obtained after the input [31,32]. e corresponding location of the area is estimated.…”

Section: Construct An Attention Modelmentioning

confidence: 99%

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

2021

Mobile Information Systems

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Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

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Section: Construct An Attention Modelmentioning

confidence: 99%

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

2021

Mobile Information Systems

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“…Medical image segmentation and diagnosis are some of the important fields in which AI image processing and pattern recognition technology is applied, and automatic image-based diagnosis has been used for a variety of disease areas, including lung cancer, rectal cancer, nasopharyngeal cancer, etc. In recent years, AI has also been introduced into oral and maxillofacial clinical diagnosis and treatment to provide valuable auxiliary information for medical professionals through automatic detection, prediction, or proposal of treatment plans [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Junling Bai, Hongchao Ma, Yuanxia Shao and Juan Shang. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] …”

mentioning

confidence: 99%

Research on Dental Chairside Scanning and Diagnostic System Based on Artificial Intelligence and Deep Learning

Bai,

Ma,

Shao

et al. 2024

Applied Mathematics and Nonlinear Sciences

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The scanning technology and diagnosis and treatment system of dental chairs play a better auxiliary role in the level of oral diagnosis and treatment as well as the doctor’s therapeutic effect. In this paper, we design an oral chairside scanning and diagnosis and treatment system, which includes two modules for scanning, acquiring, and managing oral image information. Taking CBCT as the main technology, the steps of image reconstruction, generation, estimation of tooth main axis, and reconstruction of independent tooth contour are carried out sequentially to obtain the three-dimensional network model of teeth. Through the dental clinic information management system, the case base is effectively managed to enhance operational efficiency. The system presented in this paper is tested and applied to analyze its effects during usage. The results show that the maximum difference between the measurement of this system and the manual measurement is not more than 1mm. The mean values of the errors of the system designed in this paper in the angular error and apical error are 3.547±1.556mm and 1.154±0.265mm, respectively. The P-value is less than or equal to 0.05, which is significant, and the system performs very well in these two aspects.

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“…10 AI models have also been applied to tray-size selection, demonstrating high precision (92.31%), recall (91.75%), and accuracy (91.75%). 11 However, AI research in maxillofacial prosthetics has focused only on predicting the color of maxillofacial prostheses 12,13 and planning molding plates for patients with cleft lip and palate. [14][15][16][17] When a diagnostic AI tool will be used, empirical evaluation is commonly regarded as the most effective approach to decide which tool, with which network architecture, to use.…”

mentioning

confidence: 99%

Advancing maxillofacial prosthodontics by using pre‐trained convolutional neural networks: Image‐based classification of the maxilla

Ali,

Sumita,

Wakabayashi

2024

Journal of Prosthodontics

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PurposeThe study aimed to compare the performance of four pre‐trained convolutional neural networks in recognizing seven distinct prosthodontic scenarios involving the maxilla, as a preliminary step in developing an artificial intelligence (AI)‐powered prosthesis design system.Materials and methodsSeven distinct classes, including cleft palate, dentulous maxillectomy, edentulous maxillectomy, reconstructed maxillectomy, completely dentulous, partially edentulous, and completely edentulous, were considered for recognition. Utilizing transfer learning and fine‐tuned hyperparameters, four AI models (VGG16, Inception‐ResNet‐V2, DenseNet‐201, and Xception) were employed. The dataset, consisting of 3541 preprocessed intraoral occlusal images, was divided into training, validation, and test sets. Model performance metrics encompassed accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and confusion matrix.ResultsVGG16, Inception‐ResNet‐V2, DenseNet‐201, and Xception demonstrated comparable performance, with maximum test accuracies of 0.92, 0.90, 0.94, and 0.95, respectively. Xception and DenseNet‐201 slightly outperformed the other models, particularly compared with InceptionResNet‐V2. Precision, recall, and F1 scores exceeded 90% for most classes in Xception and DenseNet‐201 and the average AUC values for all models ranged between 0.98 and 1.00.ConclusionsWhile DenseNet‐201 and Xception demonstrated superior performance, all models consistently achieved diagnostic accuracy exceeding 90%, highlighting their potential in dental image analysis. This AI application could help work assignments based on difficulty levels and enable the development of an automated diagnosis system at patient admission. It also facilitates prosthesis designing by integrating necessary prosthesis morphology, oral function, and treatment difficulty. Furthermore, it tackles dataset size challenges in model optimization, providing valuable insights for future research.

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Dental Impression Tray Selection From Maxillary Arch Images Using Multi-Feature Fusion and Ensemble Classifier

Cited by 7 publications

References 57 publications

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

Research on Dental Chairside Scanning and Diagnostic System Based on Artificial Intelligence and Deep Learning

Advancing maxillofacial prosthodontics by using pre‐trained convolutional neural networks: Image‐based classification of the maxilla

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