Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique

Ariji, Yoshiko; Yanashita, Yudai; Kutsuna, Syota; Muramatsu, Chisako; Fukuda, Motoki; Kise, Yoshitaka; Nozawa, Michihito; Kuwada, Chiaki; Fujita, Hiroshi; Katsumata, Akitoshi; Ariji, Eiichiro

doi:10.1016/j.oooo.2019.05.014

Cited by 142 publications

(94 citation statements)

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“…The main advantage of panoramic radiography is the ability to detect tooth-and jaw-related objects simultaneously [27]. Despite the plethora of images available, few studies [19,[28][29][30][31] have applied CNNs to their classifications and diagnoses. Studies that used panoramic radiographs often involved diseases related to the jawbone [28,29,31] and the maxillary sinus [19].…”

Section: Discussionmentioning

confidence: 99%

“…Despite the plethora of images available, few studies [19,[28][29][30][31] have applied CNNs to their classifications and diagnoses. Studies that used panoramic radiographs often involved diseases related to the jawbone [28,29,31] and the maxillary sinus [19]. Because panoramic radiographs have different distortions depending on the region to be photographed, periapical radiographic images have generally been used for diagnosis, whereas CNNs have been used for tooth-related classifications and diagnoses [32,33].…”

Section: Discussionmentioning

confidence: 99%

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Deep Neural Networks for Dental Implant System Classification

et al. 2020

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In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Neural Networks for Dental Implant System Classification

et al. 2020

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“…In this way, images can be used as an input for the neural networks in order to achieve several different outputs [15]. As such, deep learning methods have already shown promising results for detecting caries [13], root fractures [9], periodontal diseases [14], for differentiating cysts and jaw tumors [8], for skeletal classification on lateral cephalograms [31], and even for improving oral cancer outcomes [32]. Regarding teeth and bone segmentation, deep learning is an encouraging approach to segment anatomical structures and later on in clinical decision making [5].…”

Section: Discussionmentioning

confidence: 99%

“…In this way, automated methods may enable faster identification and classification of data and eliminate errors associated with human fatigue. Deep learning algorithms have been investigated in dentomaxillofacial radiology for the detection, classification, or diagnosis of diseases or anatomical structures, such as classification of teeth and mandibular morphology [5][6][7]; differentiation of jaw tumors [8]; and detection of root fractures [9], Sjögren's syndrome [10], maxillary sinusitis [11], calcified carotid atheroma's [12], caries [13], and periodontal diseases [14]. Although the results of previous AI research have been extremely promising, the studies are still preliminary [15].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

et al. 2020

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Objective To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs. Materials and methods In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations. Results The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual. Conclusions The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone. Clinical significance An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.

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“…The former can achieve the acceleration effect of almost the same physical processor core, while the latter can achieve 90 times the acceleration ratio. Related scholars have proposed an accelerated training method for deep convolutional neural networks based on response reconstruction [32]. This method accelerates the training of deep convolutional networks by solving a nonlinear optimization problem with low rank constraints, and designs a generalized singular value decomposition method to solve the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Big Data Image Classification Based on Distributed Deep Representation Learning Model

Zhu

Chen

2020

IEEE Access

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Traditional image classification technology has become increasingly unable to meet the changing needs of the era of big data. With the open source use of a large number of marked databases and the development and promotion of computers with high performance, deep learning has moved from theory to practice and has been widely used in image classification. This paper takes big data image classification as the research object, selects distributed deep learning tools based on Spark cluster platform, and studies the image classification algorithm based on distributed deep learning. Aiming at the problems that the Labeled Structural Deep Network Embedding (LSDNE) model is applied to the attribute network and generates a large number of hyperparameters and the model complexity is too high, inspired by the Locally Linear Embedding (LLE) algorithm, this paper proposes a semi-supervised network based on the neighbor structure learning model. This model will add the neighbor information of the node at the same time when learning the network representation. Through the node vector reconstruction, the node itself and the neighbor node together constitute the next layer of representation. On the basis of Structural Labeled Locally Deep Nonlinear Embedding (SLLDNE), the node attribute is further added to propose Structural Informed Locally Distributed Deep Nonlinear Embedding (SILDDNE), and how the model combines the structural characteristics of the node with the attribute characteristics is explained in detail. The SVM classifier classifies the known labels, and SILDDNE fuses the network structure, labels, and node attributes into the deep neural network. The experimental results on the CIFAR-10 and CIFAR-100 datasets for image classification standard recognition tasks show that the proposed network achieves good classification performance and has a high generalization ability. Experiments on the CIFAR-10 data set show that the 34layer SLLDNE pruned 40-layer Dense Net compresses about 50% of the parameter amount, increases the computational complexity efficiency by about 8 times, and reduces the classification error rate by 30%. Experiments on the CIFAR-100 data set show that the 34-layer SLLDNE parameter volume is compressed by about 16 times compared to the 19-layer VGG parameter volume, the computational complexity efficiency is increased by about 6 times, and the classification error rate is reduced by 14%. INDEX TERMS image classification; distributed network representation learning; deep learning; neighbor reconstruction.

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Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique

Cited by 142 publications

References 20 publications

Deep Neural Networks for Dental Implant System Classification

Deep Neural Networks for Dental Implant System Classification

Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs

Big Data Image Classification Based on Distributed Deep Representation Learning Model

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