Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Dong-Woon Lee; Sung-Yong Kim; Seong-Nyum Jeong; Jae-Hong Lee

doi:10.3390/diagnostics11020233

Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽

10.3390/diagnostics11020233 ◽

2021 ◽

Vol 11 (2) ◽

pp. 233

Author(s):

Dong-Woon Lee ◽

Sung-Yong Kim ◽

Seong-Nyum Jeong ◽

Jae-Hong Lee

Keyword(s):

Dental Implant ◽

Classification Accuracy ◽

Reliability And Validity ◽

Mechanical Complication ◽

Radiographic Images ◽

Acceptable Accuracy ◽

Reliable Detection ◽

Classification Evaluation ◽

Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

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Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

Biomolecules ◽

10.3390/biom11060815 ◽

2021 ◽

Vol 11 (6) ◽

pp. 815

Author(s):

Shintaro Sukegawa ◽

Kazumasa Yoshii ◽

Takeshi Hara ◽

Tamamo Matsuyama ◽

Katsusuke Yamashita ◽

...

Keyword(s):

Deep Learning ◽

Dental Implant ◽

Area Under The Curve ◽

Treatment Phase ◽

Panoramic Radiograph ◽

Radiographic Images ◽

Treatment Stage ◽

Prefectural Central Hospital ◽

Dental Panoramic Radiograph

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

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A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study

Diagnostics ◽

10.3390/diagnostics10110910 ◽

2020 ◽

Vol 10 (11) ◽

pp. 910

Author(s):

Jae-Hong Lee ◽

Young-Taek Kim ◽

Jong-Bin Lee ◽

Seong-Nyum Jeong

Keyword(s):

Dental Implant ◽

Performance Comparison ◽

Youden Index ◽

Radiographic Images ◽

Dental Imaging ◽

Different Types ◽

Multi Center Study ◽

Index Sensitivity ◽

Dental Professionals

In this study, the efficacy of the automated deep convolutional neural network (DCNN) was evaluated for the classification of dental implant systems (DISs) and the accuracy of the performance was compared against that of dental professionals using dental radiographic images collected from three dental hospitals. A total of 11,980 panoramic and periapical radiographic images with six different types of DISs were divided into training (n = 9584) and testing (n = 2396) datasets. To compare the accuracy of the trained automated DCNN with dental professionals (including six board-certified periodontists, eight periodontology residents, and 11 residents not specialized in periodontology), 180 images were randomly selected from the test dataset. The accuracy of the automated DCNN based on the AUC, Youden index, sensitivity, and specificity, were 0.954, 0.808, 0.955, and 0.853, respectively. The automated DCNN outperformed most of the participating dental professionals, including board-certified periodontists, periodontal residents, and residents not specialized in periodontology. The automated DCNN was highly effective in classifying similar shapes of different types of DISs based on dental radiographic images. Further studies are necessary to determine the efficacy and feasibility of applying an automated DCNN in clinical practice.

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CPO Complete, a novel test for fast, accurate phenotypic detection and classification of carbapenemases

10.1101/710939 ◽

2019 ◽

Author(s):

Gina K. Thomson ◽

Sameh AbdelGhani ◽

Kenneth S. Thomson

Keyword(s):

Pseudomonas Aeruginosa ◽

Acinetobacter Baumannii ◽

Sensitivity And Specificity ◽

Classification Accuracy ◽

Limited Resource ◽

Complete Test ◽

Classification Evaluation ◽

Automated Instruments ◽

Lactamase Inhibitor

AbstractRapid, accurate detection of carbapenemase-producing organisms (CPOs) and the classification of their carbapenemases are valuable tools for reducing the mortality of the CPO-associated infections, preventing the spread of CPOs, and optimizing use of new β-lactamase inhibitor combinations such as ceftazidime/avibactam and meropenem/vaborbactam. The current study evaluated the performance of CPO Complete, a novel, manual, phenotypic carbapenemase detection and classification test. The test was evaluated for sensitivity and specificity against 262 CPO isolates of Enterobacteriaceae, Pseudomonas aeruginosa and Acinetobacter baumannii and 67 non-CPO isolates. It was also evaluated for carbapenemase classification accuracy against 205 CPOs that produced a single carbapenemase class. The test exhibited 100% sensitivity 98.5% specificity for carbapenemase detection within 90 minutes and detected 74.1% of carbapenemases within 10 minutes. In the classification evaluation, 99.0% of carbapenemases were correctly classified. The test is technically simple and has potential for adaptation to automated instruments. With lyophilized kit storage at temperatures up to 38°C the CPO Complete test has the potential to provide rapid, accurate carbapenemase detection and classification in both limited resource and technologically advanced laboratories.

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Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1845 ◽

2020 ◽

Vol 4 (2) ◽

pp. 377-383

Author(s):

Eko Laksono ◽

Achmad Basuki ◽

Fitra Bachtiar

Keyword(s):

Frequency Distribution ◽

Confusion Matrix ◽

High Accuracy ◽

K Value ◽

A Value ◽

Optimal Value ◽

Classification Evaluation ◽

Email Spam ◽

Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

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An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽

10.3390/info12060249 ◽

2021 ◽

Vol 12 (6) ◽

pp. 249

Author(s):

Xin Jin ◽

Yuanwen Zou ◽

Zhongbing Huang

Keyword(s):

Cell Cycle ◽

Deep Learning ◽

Image Classification ◽

Classification Accuracy ◽

Data Augmentation ◽

Cycle Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽

10.3390/s19040916 ◽

2019 ◽

Vol 19 (4) ◽

pp. 916 ◽

Cited By ~ 2

Author(s):

Wen Cao ◽

Chunmei Liu ◽

Pengfei Jia

Keyword(s):

Feature Extraction ◽

Kernel Function ◽

Electronic Nose ◽

Classification Accuracy ◽

Extraction Methods ◽

Object Function ◽

Optimal Value ◽

Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

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ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

Complex & Intelligent Systems ◽

10.1007/s40747-021-00347-4 ◽

2021 ◽

Author(s):

Xiongzhi Ai ◽

Jiawei Zhuang ◽

Yonghua Wang ◽

Pin Wan ◽

Yu Fu

Keyword(s):

Papillary Carcinoma ◽

Network Model ◽

Reliability And Validity ◽

Thyroid Papillary Carcinoma ◽

Data Set ◽

Ultrasonic Image ◽

First Choice ◽

Thyroid Papillary ◽

Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

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Classification of SSVEP-based BCIs using Genetic Algorithm

Journal Of Big Data ◽

10.1186/s40537-021-00478-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hamideh Soltani ◽

Zahra Einalou ◽

Mehrdad Dadgostar ◽

Keivan Maghooli

Keyword(s):

Genetic Algorithm ◽

Dimension Reduction ◽

Classification Accuracy ◽

Bayesian Method ◽

Computer Interface ◽

Support Vector ◽

Svm Classifier ◽

Effective Dimension ◽

Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

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Improved differentiation classification of variable precision artificial intelligence higher education management

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219036 ◽

2021 ◽

pp. 1-10

Author(s):

Chao Dong ◽

Yan Guo

Keyword(s):

Artificial Intelligence ◽

Higher Education ◽

Data Mining ◽

Decision Tree ◽

Classification Accuracy ◽

Attribute Selection ◽

Higher Education Management ◽

Education Management ◽

Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

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