A Convolutional Neural Network Based Auto-Positioning Method For Dental Arch In Rotational Panoramic Radiography

Dental panoramic radiography (DPR) is a method commonly used in dentistry for patient diagnosis. This study presents a new technique that combines a regional convolutional neural network (RCNN), Single Shot Multibox Detector, and heuristic methods to detect and number the teeth and implants with only fixtures in a DPR image. This technology is highly significant in providing statistical information and personal identification based on DPR and separating the images of individual teeth, which serve as basic data for various DPR-based AI algorithms. As a result, the mAP(@IOU = 0.5) of the tooth, implant fixture, and crown detection using the RCNN algorithm were obtained at rates of 96.7%, 45.1%, and 60.9%, respectively. Further, the sensitivity, specificity, and accuracy of the tooth numbering algorithm using a convolutional neural network and heuristics were 84.2%, 75.5%, and 84.5%, respectively. Techniques to analyze DPR images, including implants and bridges, were developed, enabling the possibility of applying AI to orthodontic or implant DPR images of patients.

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An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs

BMC Medical Imaging ◽

10.1186/s12880-021-00656-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Elif Bilgir ◽

İbrahim Şevki Bayrakdar ◽

Özer Çelik ◽

Kaan Orhan ◽

Fatma Akkoca ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Convolutional Neural Network ◽

False Positive ◽

Panoramic Radiography ◽

False Negative ◽

True Positive ◽

Data Set ◽

Panoramic Radiographs ◽

F Measure

Abstract Background Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs. Methods The data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix. Results The total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively. Conclusions The deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making.

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Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography

Oral Radiology ◽

10.1007/s11282-018-0363-7 ◽

2018 ◽

Vol 35 (3) ◽

pp. 301-307 ◽

Cited By ~ 25

Author(s):

Makoto Murata ◽

Yoshiko Ariji ◽

Yasufumi Ohashi ◽

Taisuke Kawai ◽

Motoki Fukuda ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Panoramic Radiography ◽

Maxillary Sinusitis

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Convolutional Neural Network for Voltage Sag Source Azimuth Recognition in Electrical Internet of Things

Wireless Communications and Mobile Computing ◽

10.1155/2021/6656564 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ding Kai ◽

Li Wei ◽

Sun Jianfeng ◽

Xiao Xianyong ◽

Wang Ying

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Extraction ◽

Short Circuit ◽

Source Location ◽

Monitoring Data ◽

Voltage Sag ◽

Multiple Points ◽

The Matrix ◽

Positioning Method

Recognition and analytics at the edge enable utility companies to predict and prevent problems in real time. Clearing the voltage sag disturbance source by the positioning method is the most effective way to solve and improve the voltage sag. However, for different grid structures and fault types, the existing methods usually achieve a sag source location based on the single feature of monitoring data extraction. However, due to the effectiveness and applicability of the existing method features, this paper proposes a multidimensional feature of the voltage sag source positioning method of the matrix. Based on the analysis of the characteristics of the voltage sag event caused by the fault, this paper proposes a multidimensional feature matrix for voltage sag source location, based on the convolutional neural network to establish the mapping relationship between the feature matrix and the voltage sag position, thus achieving multiple points based on multiple points. The voltage sag source orientation is identified by the monitoring data. Finally, the voltage sag event caused by the short-circuit fault is simulated in the IEEE14 node model, and the effectiveness of the proposed method is verified by simulation data. The simulation results show that the proposed method has higher accuracy than the traditional method, and the method can be applied to different grid structures and different types of faults.

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CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

10.1055/s-0040-1705059 ◽

2020 ◽

Author(s):

S Kashin ◽

D Zavyalov ◽

A Rusakov ◽

V Khryashchev ◽

A Lebedev

Keyword(s):

Neural Network ◽

Convolutional Neural Network

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No-Reference Utility Estimation with a Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2018.09.iriacv-202 ◽

2018 ◽

Vol 2018 (9) ◽

pp. 202-1-202-6 ◽

Cited By ~ 2

Author(s):

Edward T. Scott ◽

Sheila S. Hemami

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Utility Estimation

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Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-180 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 181-1-181-7

Author(s):

Takahiro Kudo ◽

Takanori Fujisawa ◽

Takuro Yamaguchi ◽

Masaaki Ikehara

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Large Scale ◽

Blind Deconvolution ◽

Training Dataset ◽

Image Deconvolution ◽

Classic Problem ◽

Key Points ◽

Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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