Log in

Relevant bibliographies by topics / Dental X-ray images / Journal articles

To see the other types of publications on this topic, follow the link: Dental X-ray images.

Journal articles on the topic 'Dental X-ray images'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Dental X-ray images.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Yaxin, GG, and CA Hargreaves. "Dental Teeth X-Ray Image Classification Using AI." Series of Clinical and Biomedical Research 2, no. 1 (2025): 1–19. https://doi.org/10.54178/2997-2701.v2i1a2004.

Full text

Abstract:

The use of artificial intelligence (AI) and machine learning (ML) in healthcare has seen significant growth in recent years. In this study, we explored the potential of deep learning techniques for dental teeth detection and the identification of teeth as “normal”, “implant”, “root”, “erupting”, and “missing” using X-ray images. Traditionally, dentists rely on visual-tactile methods to diagnose oral conditions. However, these methods have limitations, such as inefficiency in time spent on diagnosis, the high cost of diagnosis, and subjectivity in the diagnosis. To address these limitations, we

APA, Harvard, Vancouver, ISO, and other styles

2

Ponomarenko, Mykola, Oleksandr Miroshnichenko, Vladimir Lukin, Sergii Krivenko, and Karen Egiazarian. "Blind denoising of dental X-ray images." Electronic Imaging 35, no. 9 (2023): 299–1. http://dx.doi.org/10.2352/ei.2023.35.9.ipas-299.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Zhang, Chen, and Michael Wilson. "Research on Dental Disease Recognition Based on Convolutional Neural Network." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1938–42. http://dx.doi.org/10.1166/jmihi.2020.3098.

Full text

Abstract:

Objective: To analyze and evaluate X-ray to help dentists formulate scientific Dental disease treatment program. Methods: A number of patients who came to our hospital for dental disease treatment were selected. The convolutional neural network and neural network algorithm were used to segment the patient’s dental X-ray images to achieve dental image analysis and determine dental disease. Results: The result 13.3% higher than that is confirming the validity of the convolutional neural network in X-ray images of dental diseases. Conclusion: Technology is a feasible method for diagnosing dental

APA, Harvard, Vancouver, ISO, and other styles

4

Avuçlu, Emre, and Fatih Basciftci. "Creating Database With Image Processing Methods From Dental X-Ray." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 14, no. 1 (2025): 56–68. https://doi.org/10.17798/bitlisfen.1526001.

Full text

Abstract:

The characteristic distinguishing features of a person define that person. With these characteristics, a person can be distinguished from other persons. Forensic sciences have to identify individuals in some cases. In identification, dental images are frequently used today, especially in age and gender determination procedures. In this study, a data base was created in which panoramic dental x-ray images could be used to identify people. By removing the borders from panoramic dental X-ray images, a total of 1313 dental images and 162 distinct tooth groups were generated. These images have unde

APA, Harvard, Vancouver, ISO, and other styles

5

Tuan, Tran Manh, Nguyen Thanh Duc, Pham Van Hai, and Le Hoang Son. "Dental Diagnosis from X-Ray Images using Fuzzy Rule-Based Systems." International Journal of Fuzzy System Applications 6, no. 1 (2017): 1–16. http://dx.doi.org/10.4018/ijfsa.2017010101.

Full text

Abstract:

In practical dentistry, dentists use their experience to examine dental X-ray images and to derive symptoms from patients for concluding possible diseases. This method is based solely on the own dentists' experience. Dental diagnosis from X-Ray images is proposed to support for dentists in their decision making. This paper presents an application of consultant system for dental diagnosis from X-Ray images based on fuzzy rule. Fuzzy rule was applied in many applications and has important role in computational intelligence, data mining, machine learning, etc. Based on a dental X-ray image datase

APA, Harvard, Vancouver, ISO, and other styles

6

Mualla, Noor, Essam H Houssein, and M. R. Hassan. "Dental Age Estimation Based on X-ray images." Computers, Materials & Continua 62, no. 2 (2020): 591–605. http://dx.doi.org/10.32604/cmc.2020.08580.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Sewerin, Ib P. "Mechanically induced images on dental x-ray film." Oral Surgery, Oral Medicine, Oral Pathology 63, no. 2 (1987): 241–48. http://dx.doi.org/10.1016/0030-4220(87)90321-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Chen, Ivane Delos Santos, Chieh-Ming Yang, Mei-Juan Chen, Ming-Chin Chen, Ro-Min Weng, and Chia-Hung Yeh. "Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images." Bioengineering 10, no. 8 (2023): 911. http://dx.doi.org/10.3390/bioengineering10080911.

Full text

Abstract:

Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is automatically detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-

APA, Harvard, Vancouver, ISO, and other styles

9

Erdelyi, Ralph-Alexandru, Virgil-Florin Duma, Cosmin Sinescu, George Mihai Dobre, Adrian Bradu, and Adrian Podoleanu. "Optimization of X-ray Investigations in Dentistry Using Optical Coherence Tomography." Sensors 21, no. 13 (2021): 4554. http://dx.doi.org/10.3390/s21134554.

Full text

Abstract:

The most common imaging technique for dental diagnoses and treatment monitoring is X-ray imaging, which evolved from the first intraoral radiographs to high-quality three-dimensional (3D) Cone Beam Computed Tomography (CBCT). Other imaging techniques have shown potential, such as Optical Coherence Tomography (OCT). We have recently reported on the boundaries of these two types of techniques, regarding. the dental fields where each one is more appropriate or where they should be both used. The aim of the present study is to explore the unique capabilities of the OCT technique to optimize X-ray

APA, Harvard, Vancouver, ISO, and other styles

10

Yadalam, Pradeep Kumar, Raghavendra Vamsi Anegundi, Mario Alberto Alarcón-Sánchez, and Artak Heboyan. "Classification and detection of dental images using meta-learning." World Journal of Clinical Cases 12, no. 32 (2024): 6559–62. http://dx.doi.org/10.12998/wjcc.v12.i32.6559.

Full text

Abstract:

Meta-learning of dental X-rays is a machine learning technique that can be used to train models to perform new tasks quickly and with minimal input. Instead of just memorizing a task, this is accomplished through teaching a model how to learn. Algorithms for meta-learning are typically trained on a collection of training problems, each of which has a limited number of labelled instances. Multiple X-ray classification tasks, including the detection of pneumonia, coronavirus disease 2019, and other disorders, have demonstrated the effectiveness of meta-learning. Meta-learning has the benefit of

APA, Harvard, Vancouver, ISO, and other styles

11

Temirova, Xosiyat Farxod qizi. "DEVELOPING A PROSTHETIC, HEALTHY, AND FILL-IN DETECTION MODEL FROM X-RAY DENTAL IMAGES USING MODERN ELECTRONIC COMPUTING MACHINES." INTERNATIONAL SCIENTIFIC-ELECTRONIC JOURNAL "PIONEERING STUDIES AND THEORIES" 1, no. 4 (2025): 4–11. https://doi.org/10.5281/zenodo.15046143.

Full text

Abstract:

This paper presents a novel deep learning model for detecting prosthetic, healthy, and fill-in regions in dental X- ray images using modern electronic computing machines. The model achieves state-of-the-art performance in accurate and efficient detection of dental structures, assisting dentists in diagnosis and treatment planning. The proposed model for prosthetic, healthy, and fill-in detection in dental X-ray images using modern electronic computing machines has several significant implications: Improved Diagnostic Accuracy: The model provides highly accurate detection of dental structures,

APA, Harvard, Vancouver, ISO, and other styles

12

Li, Boyuan, Derrek Spronk, Yueting Luo, et al. "Feasibility of dual-energy CBCT by spectral filtration of a dual-focus CNT x-ray source." PLOS ONE 17, no. 2 (2022): e0262713. http://dx.doi.org/10.1371/journal.pone.0262713.

Full text

Abstract:

Cone beam computed tomography (CBCT) is now widely used in dentistry and growing areas of medical imaging. The presence of strong metal artifacts is however a major concern of using CBCT especially in dentistry due to the presence of highly attenuating dental restorations, fixed appliances, and implants. Virtual monoenergetic images (VMIs) synthesized from dual energy CT (DECT) datasets are known to reduce metal artifacts. Although several techniques exist for DECT imaging, they in general come with significantly increased equipment cost and not available in dental clinics. The objectives of t

APA, Harvard, Vancouver, ISO, and other styles

13

Rashid, Umer, Aiman Javid, Abdur Rehman Khan, et al. "A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images." PeerJ Computer Science 8 (February 18, 2022): e888. http://dx.doi.org/10.7717/peerj-cs.888.

Full text

Abstract:

Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists’ interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary c

APA, Harvard, Vancouver, ISO, and other styles

14

Jufriadif, Na`am, Harlan Johan, Madenda Sarifuddin, and Prasetyo Wibowo Eri. "Image Processing of Panoramic Dental X-Ray for Identifying Proximal Caries." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 2 (2017): 702–8. https://doi.org/10.12928/TELKOMNIKA.v15i2.4622.

Full text

Abstract:

This study aims to facilitate the identification of proximal caries in the Panoramic Dental X-Ray image. Twenty-seven X-Ray images of proximal caries were elaborated. The images in digital form were processed using Matlab and Multiple Morphological Gradients. The process produced sharper images and clarifies the edges of the objects in the images. This makes the characteristics of the proximal caries and the caries severity can be identified precisely.

APA, Harvard, Vancouver, ISO, and other styles

15

Jain, Anil K., and Hong Chen. "Matching of dental X-ray images for human identification." Pattern Recognition 37, no. 7 (2004): 1519–32. http://dx.doi.org/10.1016/j.patcog.2003.12.016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Alharbi, Shuaa S., Athbah A. AlRugaibah, Haifa F. Alhasson, and Rehan Ullah Khan. "Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models." Applied Sciences 13, no. 23 (2023): 12771. http://dx.doi.org/10.3390/app132312771.

Full text

Abstract:

Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries l

APA, Harvard, Vancouver, ISO, and other styles

17

Tuan, Tran Manh, Le Hoang Son, and Le Ba Dung. "Dynamic semi-supervised fuzzy clustering for dental X-ray image segmentation: an analysis on the additional function." Journal of Computer Science and Cybernetics 31, no. 4 (2016): 323. http://dx.doi.org/10.15625/1813-9663/31/4/7234.

Full text

Abstract:

Dental X-ray image segmentation is a necessary and important process in medical diagnosis, which assists clinicians to make decisions about possible dental diseases of a patient from a dental X-ray image. It is a multi-objective optimization problem which involves basic components of fuzzy clustering, spatial structures of a dental image, and additional information of experts expressed through a pre-defined membership matrix. In our previous work, the authors presented a semi-supervised fuzzy clustering algorithm using interactive fuzzy satisficing named as SSFC-FS for this problem. An importa

APA, Harvard, Vancouver, ISO, and other styles

18

Almalki, Yassir Edrees, Amsa Imam Din, Muhammad Ramzan, et al. "Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images." Sensors 22, no. 19 (2022): 7370. http://dx.doi.org/10.3390/s22197370.

Full text

Abstract:

The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In

APA, Harvard, Vancouver, ISO, and other styles

19

Majanga, Vincent, and Serestina Viriri. "A Survey of Dental Caries Segmentation and Detection Techniques." Scientific World Journal 2022 (April 11, 2022): 1–19. http://dx.doi.org/10.1155/2022/8415705.

Full text

Abstract:

Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found

APA, Harvard, Vancouver, ISO, and other styles

20

Pratik, S. Meshram, K. Parate Dhanashree, B. Jawale Chetan, V. Yewate Akshay, and Lokhande Netra. "A Machine Learning Access for Person Identification using Dental X-Ray Images." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 185–89. https://doi.org/10.35940/ijrte.B3363.079220.

Full text

Abstract:

Dental radiographs do a great deal of work on the evidence of criminal classification. Science deontology is used in crimes that deal with the evidence of a person's separation related to dental exposure. Due to the advances in data design and the need to evaluate more cases by legal professionals, it is important to use a human evidence framework. Dental radiographs can be classified as biometric if there are no alternatives to body biometrics, for example, palm, finger, iris, face, leg print, and so on. The human body seen using dental radiographs is best under certain conditions when th

APA, Harvard, Vancouver, ISO, and other styles

21

Amir, Asmiati, Muhammad Yunus, Septiana Kurniasari, and Citron Supu Payu. "DENTAL X-RAY RADIATION EXPOSURE IN RADIOPROTECTION CASES AND DENTAL IMAGES REPEATABILITY FACTORS." EDUPROXIMA : Jurnal Ilmiah Pendidikan IPA 6, no. 1 (2024): 1–7. http://dx.doi.org/10.29100/.v6i1.5123.

Full text

Abstract:

This research was carried out using a literature review method to determine radioprotection cases and repeatability factors of dental images on dental X-rays. Determination of the repeatability factor for dental photographs using data over three months obtained at Rajawali Citra Hospital, RSUD Dr. Tjitrowardojo Purworejo, and RSUD Muntilan Magelang. Case A, case B, and case C produce high percentage values. This is because dental practitioners consider the provision of protection in reducing the scattered dose of dental X-rays for patients to be often overlooked. Determination of X-ray radiati

APA, Harvard, Vancouver, ISO, and other styles

22

Shashikala, J., and N. Thangadurai. "Evaluating spatial and frequency domain enhancement techniques on dental images to assist dental implant therapy." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5019–33. https://doi.org/10.11591/ijece.v11i6.pp5019-5033.

Full text

Abstract:

Dental imaging provides the patient's anatomical details for the dental implant based on the maxillofacial structure and the two-dimensional geometric projection, helping clinical experts decide whether the implant surgery is suitable for a particular patient. Dental images often suffer from problems associated with random noise and low contrast factors, which need effective preprocessing operations. However, each enhancement technique comes with some advantages and limitations. Therefore, choosing a suitable image enhancement method always a difficult task. In this paper, a universal fram

APA, Harvard, Vancouver, ISO, and other styles

23

Hasnain, Muhammad Adnan, Zeeshan Ali, Asif Saeed, Sadaf Aijaz, and Muhammad Saeed Khurram. "PDDNet: Deep Learning Based Dental Disease Classification through Panoramic Radiograph Images." VFAST Transactions on Software Engineering 12, no. 4 (2024): 180–98. https://doi.org/10.21015/vtse.v12i4.2028.

Full text

Abstract:

The high prevalence of dental cavities is a global public health concern. If untreated, cavities can lead to tooth loss, but timely detection and treatment can prevent this outcome. X-ray imaging provides crucial insights into the structure of teeth and surrounding tissues, enabling dentists to identify issues that may not be immediately visible. However, manual assessment of dental X-rays is time-consuming and prone to errors due to variations in dental structures and limited expertise. Automated analysis technology can reduce dentists’ workload and improve diagnostic accuracy. This study pro

APA, Harvard, Vancouver, ISO, and other styles

24

Kawamoto, Luiz Teruo, Waltraudi Orchulhak Kawamoto, Alexandre Formigoni, Enio Fernandes Rodrigues, Ivan Pérsio de Arruda Campos, and Silvia Cristina Martini Rodrigues. "Quality Comparison of Analog and Digital X-Ray Equipment and Materials in a Dental Clinic." Key Engineering Materials 660 (August 2015): 330–34. http://dx.doi.org/10.4028/www.scientific.net/kem.660.330.

Full text

Abstract:

Many dental clinics are swapping analog x-ray equipment for digital systems to obtain medical images in the search for improvements such as reduced costs and better care of the images and fast response. However it is necessary to analyze if the digital images have quality requirements in order to provide the diagnosis in a similar or superior way to the analog films. The objective of this paper is to analyze the quality of the digitized x-ray diagnostic images in relation to the structures represented. The research was conducted in a dental clinic that was changing the analog x-ray equipment f

APA, Harvard, Vancouver, ISO, and other styles

25

Lira, Pedro H. M., Gilson A. Giraldi, and Luiz A. P. Neves. "Segmentation and Feature Extraction of Panoramic Dental X-Ray Images." International Journal of Natural Computing Research 1, no. 4 (2010): 1–15. http://dx.doi.org/10.4018/jncr.2010100101.

Full text

Abstract:

Automating the process of analysis of Panoramic X-Ray images is important to help dentist procedures and diagnosis. Tooth segmentation from the radiographic images and feature extraction are essential steps. The authors propose a segmentation approach based on mathematical morphology, quadtree decomposition for mask generation, thresholding, and snake models. The feature extraction stage is steered by a shape model based on Principal Component Analysis (PCA). First, the authors take the quadtree decomposition of a low-pass version of the original image and select the smallest blocks to generat

APA, Harvard, Vancouver, ISO, and other styles

26

Kumar, Anuj, Harvendra Singh Bhadauria, and Annapurna Singh. "Descriptive analysis of dental X-ray images using various practical methods: A review." PeerJ Computer Science 7 (September 13, 2021): e620. http://dx.doi.org/10.7717/peerj-cs.620.

Full text

Abstract:

In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-

APA, Harvard, Vancouver, ISO, and other styles

27

S, Vidhya, Vijayakumari B, and Saraya J. "DEEP LEARNING BASED GENDER CLASSIFICATION WITH DENTAL X-RAY IMAGES." International Journal of Biomedical Engineering and Technology 41, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbet.2023.10050046.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

J. Attia, Salim, and Suhad S. Hussein. "Evaluation of Image Enhancement Techniques of Dental X-Ray Images." Indian Journal of Science and Technology 10, no. 40 (2017): 1–5. http://dx.doi.org/10.17485/ijst/2017/v10i40/115810.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Geetha, V., and K. S. Aprameya. "Dental Caries Diagnosis in X-ray Images Using KNN Classifier." Indian Journal of Science and Technology 12, no. 4 (2019): 1–5. http://dx.doi.org/10.17485/ijst/2019/v12i4/139880.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Vijayakumari, B., S. Vidhya, and J. Saranya. "Deep learning-based gender classification with dental X-ray images." International Journal of Biomedical Engineering and Technology 42, no. 1 (2023): 109–21. http://dx.doi.org/10.1504/ijbet.2023.131694.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Ølberg, Jan-Vidar, and Morten Goodwin. "Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation." Scandinavian Journal of Forensic Science 22, no. 2 (2016): 44–56. http://dx.doi.org/10.1515/sjfs-2016-0008.

Full text

Abstract:

Abstract Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted. This paper proposes a new system for autom

APA, Harvard, Vancouver, ISO, and other styles

32

Anderla, Andras, Dubravko Culibrk, Gaspar Delso, and Milan Mirkovic. "MR Image Based Approach for Metal Artifact Reduction in X-Ray CT." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/524243.

Full text

Abstract:

For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic implants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper, we propose a new method for metal-artifact reduction using complementary magnetic resonance (MR) images. The method exploits the possibilities which arise from the use of emergent trimodality systems. The proposed algorithm corrects reconstructed CT images. The projected data which is affected by dental fillings is detected and the missing pr

APA, Harvard, Vancouver, ISO, and other styles

33

Na`am, Jufriadif. "Accuracy of Panoramic Dental X-Ray Imaging in Detection of Proximal Caries with Multiple Morpological Gradient (mMG) Method." JOIV : International Journal on Informatics Visualization 1, no. 1 (2017): 5. http://dx.doi.org/10.30630/joiv.1.1.13.

Full text

Abstract:

Dental caries is tooth decay caused by bacterial infection. This is commonly known as tooth decay. Classification of caries by location consists of; occlusal caries, proximal caries, root caries and caries enamel. Diagnosis of dental caries in general carried out with the help of radiographic images is called Dental X-Ray. Dental X-Ray consists of bitewing, Periapical and Panoramic. Identification of proximal caries using Dental Panoramic X-Ray lowest precision was compared with both other Dental X-Ray. This study aims to perform sharpening and improving the quality of information contained in

APA, Harvard, Vancouver, ISO, and other styles

34

Huang, Ya-Yun, Chiung-An Chen, Yi-Cheng Mao, et al. "An Integrated System for Detecting and Numbering Permanent and Deciduous Teeth Across Multiple Types of Dental X-Ray Images Based on YOLOv8." Diagnostics 15, no. 13 (2025): 1693. https://doi.org/10.3390/diagnostics15131693.

Full text

Abstract:

Background/Objectives: In dental medicine, the integration of various types of X-ray images, such as periapical (PA), bitewing (BW), and panoramic (PANO) radiographs, is crucial for comprehensive oral health assessment. These complementary imaging modalities provide diverse diagnostic perspectives and support the early detection of oral diseases, thereby enhancing treatment outcomes. However, there is currently no existing system that integrates multiple types of dental X-rays for both adults and children to perform tooth localization and numbering. Methods: Therefore, this study aimed to prop

APA, Harvard, Vancouver, ISO, and other styles

35

Jannah, Marichatul, Ardi Soesilo Wibowo, Agustina Dwi Prastanti, and Wingghayarie Patra Gandhi. "IMPROVING BITEWING RADIOGRAPHY: EVALUATING THE EFFECT OF A DENTAL X-RAY POSITIONER ON IMAGE QUALITY IN DENTAL EXAMINATIONS." Jurnal Riset Kesehatan 12, no. 1 (2023): 73–80. http://dx.doi.org/10.31983/jrk.v12i1.9769.

Full text

Abstract:

Bitewing radiography is a valuable tool in detecting and monitoring dental decay and other oral health problems and is typically performed as part of a routine dental examination. It has certain limitations that should be considered. Some of these limitations may cause discomfort or pain to the patient if not positioned correctly. Some patients may find biting down on the film holder difficult or have a strong gag reflex, making the procedure uncomfortable or even impossible. Bitewing radiography can be costly, especially if it needs to be performed regularly, which may limit access to this di

APA, Harvard, Vancouver, ISO, and other styles

36

Sukegawa, Shintaro, Kazumasa Yoshii, Takeshi Hara, et al. "Deep Neural Networks for Dental Implant System Classification." Biomolecules 10, no. 7 (2020): 984. http://dx.doi.org/10.3390/biom10070984.

Full text

Abstract:

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) w

APA, Harvard, Vancouver, ISO, and other styles

37

Liu, Riming, and Zhenshan Gao. "Artificial Intelligence Precision Recognition and Auxiliary Diagnosis of Dental X-ray Panoramic Images Based on Deep Learning." BIO Web of Conferences 174 (2025): 03020. https://doi.org/10.1051/bioconf/202517403020.

Full text

Abstract:

Objective: This study aims to explore the application of deep learning algorithms in dental X-ray panoramic images, particularly for the automatic segmentation of dental caries and identification of wisdom tooth types, in order to improve the accuracy and efficiency of dental diagnosis and assist doctors in formulating precise treatment plans. Methods: Multiple classic medical image segmentation network models (including Unet, PSPNet, FPN, Unet++, and DeepLabV3+) were trained and tested on the ParaDentCaries dataset to evaluate their performance in dental X-ray panoramic images. Performance wa

APA, Harvard, Vancouver, ISO, and other styles

38

Hasnain, Muhammad Adnan, Zeeshan Ali, Muhammad Sajid Maqbool, and Musfira Aziz. "X-ray Image Analysis for Dental Disease: A Deep Learning Approach Using EfficientNets." VFAST Transactions on Software Engineering 12, no. 3 (2024): 147–65. http://dx.doi.org/10.21015/vtse.v12i3.1912.

Full text

Abstract:

Dental cavities are a highly common persistent dental problem that impacts populations across different age groups on a global scale. It is crucial to get a dental issue diagnosed as early as possible and with as much accuracy as possible to treat it efficiently and prevent any related issues. If a dental infection is not treated, it will eventually grow and cause tooth loss. Dental X-ray images are crucial and beneficial in the diagnostic process of dental diseases for dentists. By applying Deep Learning (DL) techniques to dental X-ray images, dental experts can efficiently and precisely dete

APA, Harvard, Vancouver, ISO, and other styles

39

Ahmed, Husham, Nabeel Ibrahim Ashour, Mohammed Yarub Hani, Mohammed Ali Dheyab, and Mahmood S. Jameel. "Absorbed Dose and Image Quality of X-Ray Diagnostics in Composite and Amalgam Dental Restorations." Journal of Medical Physics 50, no. 2 (2025): 407–14. https://doi.org/10.4103/jmp.jmp_216_24.

Full text

Abstract:

Aim: This study evaluates the absorbed dose from X-rays on (composite and amalgam) dental fillings and examines its impact on image quality. In addition, it investigates the potential health risks associated with X-ray usage in dental diagnostics. Materials and Methods: Composite and amalgam fillings were subjected to local low dose ionizing radiation in the oral cavity through X-ray imaging. The absorbed dose for each dental restoration material was estimated, and the quality of the resulting X-ray images was assessed. Results: The study further analyzed the relationship between absorbed dose

APA, Harvard, Vancouver, ISO, and other styles

40

Suryani, D., M. N. Shoumi, and R. Wakhidah. "Object detection on dental x-ray images using deep learning method." IOP Conference Series: Materials Science and Engineering 1073, no. 1 (2021): 012058. http://dx.doi.org/10.1088/1757-899x/1073/1/012058.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Shamraeva, E. O., A. A. Shamraev, and L. V. Polovneva. "Quality Improvement of X-Ray Contrast Images for Dental Materials Control." Intellekt. Sist. Proizv. 17, no. 4 (2020): 41. http://dx.doi.org/10.22213/2410-9304-2019-4-41-47.

Full text

Abstract:

Работа посвящена повышению объективности методов визуального контроля качества стоматологических материалов при определении их рентгеноконтрастности на примере стоматологического стеклоиономерного двухкомпонентного рентгеноконтрастного цемента «Цемион» производства ОАО «ОЭЗ «ВладМиВа». Контроль рентгеноконтрасности осуществляется на основании рентгеновских снимков эталонного алюминиевого клина и стоматологического материала. Оцифровка рентгенограмм производится с помощью сканера общего назначения. Авторами предложен и программно реализован метод для автоматической обработки оцифрованных рентге

APA, Harvard, Vancouver, ISO, and other styles

42

Potrakhov, N. N., and A. Yu Gryaznov. "Method for Assessment of Information Value of Dental X-Ray Images." Biomedical Engineering 43, no. 1 (2009): 17–19. http://dx.doi.org/10.1007/s10527-009-9086-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Jyothi, G. C., Chetana Prakash, G. A. Babitha, and G. H. Kiran Kumar. "Comparison Analysis of CNN, SVC and Random Forest Algorithms in Segmentation of Teeth X-Ray Images." Asian Journal of Computer Science and Technology 11, no. 1 (2022): 40–47. http://dx.doi.org/10.51983/ajcst-2022.11.1.3283.

Full text

Abstract:

In dental diagnosis, rapid identification of dental complications from radiographs requires highly experienced medical professionals. Occasionally, depending exclusively on a expert's judgement could lead to changes in diagnosis, that could eventually lead to difficult treatment. Although fully automatic diagnostic tools aren’t still anticipated, image pattern recognition has grown into decision support, opening with discovery of teeth and its constituents on X-ray images. Dental discovery is a topic of study for more than previous two decades, depending primarily on threshold and region-based

APA, Harvard, Vancouver, ISO, and other styles

44

Kats, Lazar, Marilena Vered, Johnny Kharouba, and Sigalit Blumer. "Transfer Deep Learning for Dental and Maxillofacial Imaging Modality Classification: A Preliminary Study." Journal of Clinical Pediatric Dentistry 45, no. 4 (2021): 233–38. http://dx.doi.org/10.17796/1053-4625-45.4.3.

Full text

Abstract:

Objective: To apply the technique of transfer deep learning on a small data set for automatic classification of X-ray modalities in dentistry. Study design: For solving the problem of classification, the convolution neural networks based on VGG16, NASNetLarge and Xception architectures were used, which received pre-training on ImageNet subset. In this research, we used an in-house dataset created within the School of Dental Medicine, Tel Aviv University. The training dataset contained anonymized 496 digital Panoramic and Cephalometric X-ray images for orthodontic examinations from CS 8100 Digi

APA, Harvard, Vancouver, ISO, and other styles

45

Khubrani, Mousa Mohammed, Fathe Jeribi, Ali Tahir, and Abdulnasser Abdulwakil Metwally. "Panoramic Dental X-Ray Restorative Elements Segmentation using Hybrid Deep Learning." WSEAS TRANSACTIONS ON COMPUTERS 23 (December 31, 2024): 328–35. https://doi.org/10.37394/23205.2024.23.32.

Full text

Abstract:

Panoramic radiography is a commonly used imaging technique for dental X-rays, it is used as a diagnostics tool in dentistry. The study introduced a hybrid deep learning approach for detecting and segmenting dental restorative elements from panoramic dental X-rays. By integrating the You Look Only Once (YOLO v8) model for object detection and the Segment Anything Model (SAM) for segmentation, the aim is to enhance the identification of different dental restorative elements such as dental implants, crowns, fillings, and root canals. The datasets of the study comprised 1290 dental X-ray images. T

APA, Harvard, Vancouver, ISO, and other styles

46

Matsuda, Shinpei, Hayato Itoi, and Hitoshi Yoshimura. "Importance of postural change for accidental ingestion of dental prostheses: a case report." Journal of International Medical Research 49, no. 8 (2021): 030006052110407. http://dx.doi.org/10.1177/03000605211040761.

Full text

Abstract:

Accidental ingestion of dental prostheses requires immediate emergency action. The authors report a case of accidental ingestion of a dental prosthesis in a patient with a disorder of consciousness. The accidental ingestion was diagnosed by imaging examination, and the location of the dental prosthesis was explored under general anesthesia according to the preoperative examination images. However, no dental prosthesis was found in the hypopharyngeal region. The operators found a radiopaque region in the nasopharynx that was suspicious of a dental prosthesis by X-ray examination of the head and

APA, Harvard, Vancouver, ISO, and other styles

47

Jyothi, G. C., Chetana Prakash, G. A. Babitha, and G. H. Kiran Kumar. "Alveolar Bone Loss Detection and Localization in Dental X-Ray Images using YOLOv5." Asian Journal of Computer Science and Technology 12, no. 1 (2023): 41–54. http://dx.doi.org/10.51983/ajcst-2023.12.1.3591.

Full text

Abstract:

Periodontal disease, characterized by alveolar bone loss, is a prevalent oral health condition that requires early detection and management to prevent further progression. This paper proposes a novel approach for alveolar bone loss detection and localization in dental X-ray images using the YOLOv5 object detection algorithm. We annotated a dataset of dental radiographs with alveolar bone loss regions and fine-tuned the YOLOv5 model on this dataset. Our approach achieved high accuracy and robustness in detecting and localizing alveolar bone loss regions, with precision, recall, and F1 score exc

APA, Harvard, Vancouver, ISO, and other styles

48

Li, Zifeng, Wenzhong Tang, Shijun Gao, Yanyang Wang, and Shuai Wang. "Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images." Entropy 26, no. 12 (2024): 1059. https://doi.org/10.3390/e26121059.

Full text

Abstract:

Dental panoramic X-ray imaging, due to its high cost-effectiveness and low radiation dose, has become a widely used diagnostic tool in dentistry. Accurate tooth segmentation is crucial for lesion analysis and treatment planning, helping dentists to quickly and precisely assess the condition of teeth. However, dental X-ray images often suffer from noise, low contrast, and overlapping anatomical structures, coupled with limited available datasets, leading traditional deep learning models to experience overfitting, which affects generalization ability. In addition, high-precision deep models typi

APA, Harvard, Vancouver, ISO, and other styles

49

Rajpoot, Vikram, Rahul Dubey, Safdar Sardar Khan, et al. "Orchard Boumans Algorithm and MRF Approach Based on Full Threshold Segmentation for Dental X-Ray Images." Traitement du Signal 39, no. 2 (2022): 737–44. http://dx.doi.org/10.18280/ts.390239.

Full text

Abstract:

Dental X-ray segmentation uses different image processing (IP) methods helpful in diagnosing medical applications, clinical purposes & in real-time. These methods aim to define the segmentation of various tooth structures in dental X-rays which are utilized to identify caries, tooth fractures, treatment of root canals, periodontal diseases, etc. The manual segmentation of Dental X-ray images for medical diagnosis is very complex and time-consuming from broad clinical databases. Orchard & Bouman is a color quantization approach used to evaluate a successful cluster division using an eig

APA, Harvard, Vancouver, ISO, and other styles

50

Sangeetha, M., Kailash Kumar, and Ahmed Abdullah Aljabr. "Image Processing Techniques in Periapical Dental X-Ray Image Detection and Classification." Webology 18, SI02 (2021): 42–53. http://dx.doi.org/10.14704/web/v18si02/web18011.

Full text

Abstract:

An Image procedure method is a method that is beneficial in involving direct observation of the patient diagnosis of determination. These are the ideas to ploy the format of the splitting of various tush forms available inside the X-radiation of dental images that would be applied for the enamel splinter, scaling, and root planning, and so on that presupposes a lead job inside the detectable evidence of infirmity. The splitting and assembling of x-radiation images of teeth for detecting, among the huge directory in clinical regular procedure, is phenomenal satisfactory in magnificence and time

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!