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Bonny T, Al-Ali A, Al-Ali M, Alsaadi R, Al Nassan W, Obaideen K, AlMallahi M. Dental bitewing radiographs segmentation using deep learning-based convolutional neural network algorithms. Oral Radiol 2024; 40:165-177. [PMID: 38047985 DOI: 10.1007/s11282-023-00717-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/11/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVES Dental radiographs, particularly bitewing radiographs, are widely used in dental diagnosis and treatment Dental image segmentation is difficult for various reasons, such as intricate structures, low contrast, noise, roughness, and unclear borders, resulting in poor image quality. Recent developments in deep learning models have improved performance in analyzing dental images. In this research, our primary objective is to determine the most effective segmentation technique for bitewing radiographs based on different metrics: accuracy, training time, and the number of training parameters as a reflection of architectural cost. METHODS In this research, we employ several deep learning models, namely Resnet-18, Resnet-50, Xception, Inception Resnet v2, and Mobilenetv2, to segment bitewing radiographs. The process begins by importing the radiographs into MATLAB®(MathWorks Inc), where the images are first improved, then segmented using the graph cut method based on regions to produce a binary mask that distinguishes the background from the original X-ray. RESULTS The deep learning models were trained on 298 and 99 radiograph training and validation sets and were evaluated using 99 images from the testing set. We also compare the segmentation model using several criteria, including accuracy, speed, and size, to determine which network is superior. Furthermore, we compare our findings with prior research to provide a comprehensive understanding of the advancements made in dental image segmentation. The accurate segmentation achieved was 93.67% and 94.42% by the Resnet-18 and Resnet-50 models, respectively. CONCLUSION This research advances dental image analysis and facilitates more accurate diagnoses and treatment planning by determining the best segmentation technique. The outcomes of this study can guide researchers and practitioners in selecting appropriate segmentation methods for practical dental image analysis.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates.
| | - Abdelaziz Al-Ali
- Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Mohammed Al-Ali
- Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Rashid Alsaadi
- Electrical and Electronics Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Khaled Obaideen
- Research Institute of Science and Technology, University of Sharjah, Sharjah, United Arab Emirates
| | - Maryam AlMallahi
- Industrial Engineering and Engineering Management Department, University of Sharjah, Sharjah, United Arab Emirates
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Li X, Zhao D, Xie J, Wen H, Liu C, Li Y, Li W, Wang S. Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis. BMC Oral Health 2023; 23:1017. [PMID: 38114946 PMCID: PMC10729340 DOI: 10.1186/s12903-023-03751-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/08/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach. METHODS A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed. RESULTS Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94). CONCLUSIONS The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.
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Affiliation(s)
- Xin Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Dan Zhao
- Department of Implant Dentistry, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Jinxuan Xie
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Hao Wen
- City University of Hong Kong, Hong Kong SAR, China
| | - Chunhua Liu
- City University of Hong Kong, Hong Kong SAR, China
| | - Yajie Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Wenbin Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songlin Wang
- Salivary Gland Disease Center and Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction, Beijing Laboratory of Oral Health and Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China.
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Alzaid N, Ghulam O, Albani M, Alharbi R, Othman M, Taher H, Albaradie S, Ahmed S. Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties. Cureus 2023; 15:e47033. [PMID: 37965397 PMCID: PMC10642940 DOI: 10.7759/cureus.47033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Since the beginning of recorded history, the human brain has been one of the most intriguing structures for scientists and engineers. Over the centuries, newer technologies have been developed based on principles that seek to mimic their functioning, but the creation of a machine that can think and behave like a human remains an unattainable fantasy. This idea is now known as "artificial intelligence". Dentistry has begun to experience the effects of artificial intelligence (AI). These include image enhancement for radiology, which improves the visibility of dental structures and facilitates disease diagnosis. AI has also been utilized for the identification of periapical lesions and root anatomy in endodontics, as well as for the diagnosis of periodontitis. This review is intended to provide a comprehensive overview of the use of AI in modern dentistry's numerous specialties. The relevant publications published between March 1987 and July 2023 were identified through an exhaustive search. Studies published in English were selected and included data regarding AI applications among various dental specialties. Dental practice involves more than just disease diagnosis, including correlation with clinical findings and administering treatment to patients. AI cannot replace dentists. However, a comprehensive understanding of AI concepts and techniques will be advantageous in the future. AI models for dental applications are currently being developed.
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Affiliation(s)
- Najd Alzaid
- Dentistry, University of Hail College of Dentistry, Hail, SAU
| | - Omar Ghulam
- General Dentistry, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Modhi Albani
- Dentistry, University of Hail College of Dentistry, Hail, SAU
| | - Rafa Alharbi
- Dentistry, Taibah University College of Dentistry, Madinah, SAU
| | - Mayan Othman
- Dentistry, Taibah University College of Dentistry, Madinah, SAU
| | - Hasan Taher
- Endodontics, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Saleem Albaradie
- General Dentistry, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Suhael Ahmed
- Maxillofacial Surgery and Diagnostic Sciences, College of Medicine and Dentistry, Riyadh Elm University, Riyadh, SAU
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Fan W, Zhang J, Wang N, Li J, Hu L. The Application of Deep Learning on CBCT in Dentistry. Diagnostics (Basel) 2023; 13:2056. [PMID: 37370951 DOI: 10.3390/diagnostics13122056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user's proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.
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Affiliation(s)
- Wenjie Fan
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiaqi Zhang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Nan Wang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jia Li
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Hu
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Mehdizadeh M, Tavakoli Tafti K, Soltani P. Evaluation of histogram equalization and contrast limited adaptive histogram equalization effect on image quality and fractal dimensions of digital periapical radiographs. Oral Radiol 2023; 39:418-424. [PMID: 36076131 DOI: 10.1007/s11282-022-00654-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/31/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES This study aims to evaluate the effects of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) on periapical images and fractal dimensions in the periapical region. METHODS In this cross-sectional study, digital periapical images were selected from the archive of Dentistry School of Isfahan University of Medical Sciences. The radiographs were taken from mandibular and maxillary anterior single root teeth with healthy root and periodontium. After applying HE and CLAHE algorithms to images, two radiologists evaluated the quality of apex detection from using a 5-point Likert scale (from 5 for very good image quality to 1 for very bad image quality). Afterward, all the images were imported to the ImageJ application, and the region of interest (ROI) was specified as the region between the two central incisors. The fractal box-counting method was used to determine fractal dimensions (FD) values. Nonparametric Wilcoxon-Friedman test, Intraclass Correlation Coefficient test, T-test, and Pair T-test were performed as statistical analysis (α = 0.05). RESULTS Fifty-three radiographs were analyzed and the image quality assessments were significantly different between raw images and images after performing HE, CLAHE (p value < 0.001), and using CLAHE algorithm significantly increases image quality assessments more than HE (p value = 0.009). There was a significant difference in FD values for images after applying CLAHE and HE compared to raw images (p value < 0.001), and HE decreased the FD value significantly more than CLAHE (p value = 0.019). CONCLUSIONS Employing CLAHE and HE algorithm via OpenCV python library improves the periapical image quality, which is more significant using the CLAHE algorithm. Moreover, applying CLAHE and HE reduces trabecular bone structure detection and FD values in periapical images, especially in HE.
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Affiliation(s)
- Mojdeh Mehdizadeh
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Kioumars Tavakoli Tafti
- Dental Students' Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Parisa Soltani
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
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Nassan WA, Bonny T, Obaideen K, Hammal AA. A Customized Convolutional Neural Network for Dental Bitewing Images Segmentation. 2022 INTERNATIONAL CONFERENCE ON ELECTRICAL AND COMPUTING TECHNOLOGIES AND APPLICATIONS (ICECTA) 2022. [DOI: 10.1109/icecta57148.2022.9990564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Wafaa Al Nassan
- University of Sharjah,Computer Eng.Dept.,Sharjah,United Arab Emirates
| | - Talal Bonny
- University of Sharjah,Computer Eng.Dept.,Sharjah,United Arab Emirates
| | - Khaled Obaideen
- University of Sharjah,Sustainable Energy and Power Systems Research Centre, RISE,Sharjah,United Arab Emirates
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Vasdev D, Gupta V, Shubham S, Chaudhary A, Jain N, Salimi M, Ahmadian A. Periapical dental X-ray image classification using deep neural networks. ANNALS OF OPERATIONS RESEARCH 2022; 326:1-29. [PMID: 36157976 PMCID: PMC9483455 DOI: 10.1007/s10479-022-04961-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.
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Affiliation(s)
- Dipit Vasdev
- Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
| | - Vedika Gupta
- Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana 131001 India
| | - Shubham Shubham
- Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
| | - Ankit Chaudhary
- Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
| | - Nikita Jain
- Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
| | - Mehdi Salimi
- Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS Canada
- Center for Dynamics, Faculty of Mathematics, Technische Universität Dresden, Dresden, Germany
| | - Ali Ahmadian
- Department of Law, Economics and Human Sciences and Decisions Lab, Mediterranea University of Reggio Calabria, 89125 Reggio Calabria, Italy
- Department of Mathematics, Near East University, Nicosia, TRNC, Mersin 10, Turkey
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Rashid U, Javid A, Khan AR, Liu L, Ahmed A, Khalid O, Saleem K, Meraj S, Iqbal U, Nawaz R. A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images. PeerJ Comput Sci 2022; 8:e888. [PMID: 35494840 PMCID: PMC9044255 DOI: 10.7717/peerj-cs.888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
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 cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions' localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.
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Affiliation(s)
- Umer Rashid
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Aiman Javid
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Abdur Rehman Khan
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Leo Liu
- School of Business and Law, The Manchester Metropolitan University, Manchester, United Kingdom
| | - Adeel Ahmed
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Osman Khalid
- Department of Computer Science, COMSATS University, Islamabad, Pakistan
| | - Khalid Saleem
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Shaista Meraj
- Department of Radiology, Bolton NHS Foundation Trust, Bolton, United Kingdom
| | - Uzair Iqbal
- Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad Chiniot-Faisalabad, Pakistan
| | - Raheel Nawaz
- School of Business and Law, The Manchester Metropolitan University, Manchester, United Kingdom
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