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Lui JCL, Lam WYH, Chu CH, Yu OY. Global Research Trends in the Detection and Diagnosis of Dental Caries: A Bibliometric Analysis. Int Dent J 2024:S0020-6539(24)01469-2. [PMID: 39237399 DOI: 10.1016/j.identj.2024.08.010] [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: 07/16/2024] [Revised: 08/04/2024] [Accepted: 08/06/2024] [Indexed: 09/07/2024] Open
Abstract
This study aims to provide an overview of the global research trends in the detection and diagnosis of dental caries in the past 20 years. A literature search was conducted in the Scopus Database to retrieve studies on the diagnostic approaches for dental caries published from January 2003 to December 2023. The diagnostic approaches in the retrieved studies were examined and the studies were categorized according to the diagnostic approaches investigated. Bibliometric data including journals, countries, affiliations, authors, and numbers of citations of the publications were summarised. The publications' keyword co-occurrence was analysed using VOSviewer. This bibliometric analysis included 1879 publications investigating seven categories of caries diagnostic approaches, including visual and/or tactile (n = 459; 19%), radiation-based (n = 662; 27%), light-based (n = 771; 32%), ultrasound-based (n = 28; 1%), electric-based (n = 51; 2%), molecular-based (n = 196; 8%) diagnostic approaches, as well as AI-based diagnostic interpretation aids (n = 265; 11%). An increase in the annual number of publications on caries diagnostic approaches was observed in the past 20 years. Caries Research (n = 103) presented the highest number of publications on caries diagnostic approaches. The country with the highest number of publications was the United States (n = 1092). The University of São Paulo was the institution that published the highest number of articles (n = 195). The publication with the highest citation has been cited 932 times. VOS viewer revealed that the most frequently occurring keywords were 'Deep Learning', 'Artificial Intelligence', 'Laser Fluorescence' and 'Radiography'. This bibliometric analysis highlighted an emerging global research trend in the detection and diagnosis approaches for dental caries in the past 20 years. An evident increase in publications on molecular-based caries diagnostic approaches and AI-based diagnostic interpretation aids was perceived over the last 5 years.
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Affiliation(s)
- Jane Ching-Lam Lui
- Faculty of Dentistry, The University of Hong Kong, Hong Kong, S.A.R., China
| | - Walter Yu-Hang Lam
- Faculty of Dentistry, The University of Hong Kong, Hong Kong, S.A.R., China
| | - Chun-Hung Chu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong, S.A.R., China
| | - Ollie Yiru Yu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong, S.A.R., China.
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Hartman H, Nurdin D, Akbar S, Cahyanto A, Setiawan AS. Exploring the potential of artificial intelligence in paediatric dentistry: A systematic review on deep learning algorithms for dental anomaly detection. Int J Paediatr Dent 2024; 34:639-652. [PMID: 38297447 DOI: 10.1111/ipd.13164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/24/2023] [Accepted: 12/05/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Artificial intelligence (AI) based on deep learning (DL) algorithms has shown promise in enhancing the speed and accuracy of dental anomaly detection in paediatric dentistry. AIM This systematic review aimed to investigate the performance of AI systems in identifying dental anomalies in paediatric dentistry and compare it with human performance. DESIGN A systematic search of Scopus, PubMed and Google Scholar was conducted from 2012 to 2022. Inclusion criteria were based on problem/patient/population, intervention/indicator, comparison and outcome scheme and specific keywords related to AI, DL, paediatric dentistry, dental anomalies, supernumerary and mesiodens. Six of 3918 initial pool articles were included, assessing nine DL sub-systems that used panoramic radiographs or cone-beam computed tomography. Article quality was assessed using QUADAS-2. RESULTS Artificial intelligence systems based on DL algorithms showed promising potential in enhancing the speed and accuracy of dental anomaly detection, with an average of 85.38% accuracy and 86.61% sensitivity. Human performance, however, outperformed AI systems, achieving 95% accuracy and 99% sensitivity. Limitations included a limited number of articles and data heterogeneity. CONCLUSION The potential of AI systems employing DL algorithms is highlighted in detecting dental anomalies in paediatric dentistry. Further research is needed to address limitations, explore additional anomalies and establish the broader applicability of AI in paediatric dentistry.
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Affiliation(s)
- Henri Hartman
- Doctoral Program, Faculty of Dentistry, Universitas Padjadjaran, Bandung, Indonesia
- Department of Pediatric Dentistry, Faculty of Dentistry, Universitas Jenderal Achmad Yani, Cimahi, Indonesia
| | - Denny Nurdin
- Department of Conservative Dentistry, Faculty of Dentistry, Universitas Padjadjaran, Bandung, Indonesia
| | - Saiful Akbar
- School of Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
| | - Arief Cahyanto
- Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia
| | - Arlette Suzy Setiawan
- Department of Pediatric Dentistry, Faculty of Dentistry, Universitas Padjadjaran, Bandung, Indonesia
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Qutieshat A, Al Rusheidi A, Al Ghammari S, Alarabi A, Salem A, Zelihic M. Comparative analysis of diagnostic accuracy in endodontic assessments: dental students vs. artificial intelligence. Diagnosis (Berl) 2024; 11:259-265. [PMID: 38696271 DOI: 10.1515/dx-2024-0034] [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: 02/16/2024] [Accepted: 04/22/2024] [Indexed: 05/04/2024]
Abstract
OBJECTIVES This study evaluates the comparative diagnostic accuracy of dental students and artificial intelligence (AI), specifically a modified ChatGPT 4, in endodontic assessments related to pulpal and apical conditions. The findings are intended to offer insights into the potential role of AI in augmenting dental education. METHODS Involving 109 dental students divided into junior (54) and senior (55) groups, the study compared their diagnostic accuracy against ChatGPT's across seven clinical scenarios. Juniors had the American Association of Endodontists (AEE) terminology assistance, while seniors relied on prior knowledge. Accuracy was measured against a gold standard by experienced endodontists, using statistical analysis including Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests. RESULTS ChatGPT achieved significantly higher accuracy (99.0 %) compared to seniors (79.7 %) and juniors (77.0 %). Median accuracy was 100.0 % for ChatGPT, 85.7 % for seniors, and 82.1 % for juniors. Statistical tests indicated significant differences between ChatGPT and both student groups (p<0.001), with no notable difference between the student cohorts. CONCLUSIONS The study reveals AI's capability to outperform dental students in diagnostic accuracy regarding endodontic assessments. This underscores AIs potential as a reference tool that students could utilize to enhance their understanding and diagnostic skills. Nevertheless, the potential for overreliance on AI, which may affect the development of critical analytical and decision-making abilities, necessitates a balanced integration of AI with human expertise and clinical judgement in dental education. Future research is essential to navigate the ethical and legal frameworks for incorporating AI tools such as ChatGPT into dental education and clinical practices effectively.
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Affiliation(s)
- Abubaker Qutieshat
- Adult Restorative Dentistry, 442177 Oman Dental College , Muscat, Oman
- Restorative Dentistry, Dundee Dental Hospital and School, University of Dundee, Dundee, UK
| | | | | | | | - Abdurahman Salem
- Dental Technology, 1796 School of Health & Society, University of Bolton , Greater Manchester, UK
| | - Maja Zelihic
- Forbes School of Business and Technology, 191123 University of Arizona Global Campus , Chandler, AZ, USA
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Zanini LGK, Rubira-Bullen IRF, Nunes FDLDS. A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1824-1845. [PMID: 38429559 PMCID: PMC11300762 DOI: 10.1007/s10278-024-01054-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/19/2023] [Accepted: 01/02/2024] [Indexed: 03/03/2024]
Abstract
Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field.
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Affiliation(s)
- Luiz Guilherme Kasputis Zanini
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil.
| | | | - Fátima de Lourdes Dos Santos Nunes
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil
- School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000, São Paulo, 03828-000, São Paulo, Brazil
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Dai F, Liu Q, Guo Y, Xie R, Wu J, Deng T, Zhu H, Deng L, Song L. Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis. Oral Radiol 2024; 40:357-366. [PMID: 38393548 DOI: 10.1007/s11282-024-00739-5] [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: 03/07/2023] [Accepted: 01/03/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. MATERIALS AND METHODS Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. RESULTS The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. CONCLUSION The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.
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Affiliation(s)
- Fang Dai
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiangdong Liu
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Second Clinical Medical School, Nanchang University, Nanchang, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yuchen Guo
- The Second Clinical Medical School, Nanchang University, Nanchang, China
| | - Ruixiang Xie
- School of Life Sciences, Nanchang University, Nanchang, China
| | - Jingting Wu
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tian Deng
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hongbiao Zhu
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Libin Deng
- School of Public Health, Nanchang University, No.1299, Xuefu Avenue, Nanchang, 330000, Jiangxi, China.
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China.
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Li Song
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China.
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China.
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
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Lee HS, Yang S, Han JY, Kang JH, Kim JE, Huh KH, Yi WJ, Heo MS, Lee SS. Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:184-195. [PMID: 38158267 DOI: 10.1016/j.oooo.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/01/2023] [Accepted: 09/15/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. STUDY DESIGN A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. RESULTS The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. CONCLUSIONS The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.
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Affiliation(s)
- Han-Sol Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Su Yang
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Ji-Yong Han
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, South Korea
| | - Ju-Hee Kang
- Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, Seoul, South Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea; Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, South Korea.
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea.
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
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Kazimierczak W, Wajer R, Wajer A, Kiian V, Kloska A, Kazimierczak N, Janiszewska-Olszowska J, Serafin Z. Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy. J Clin Med 2024; 13:2709. [PMID: 38731237 PMCID: PMC11084607 DOI: 10.3390/jcm13092709] [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: 04/23/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI's diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI's sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI's sensitivity was 77.78% with an F1 score of 84.00%. The AI's specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Róża Wajer
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| | - Adrian Wajer
- Dental Primus, Poznańska 18, 88-100 Inowrocław, Poland
| | - Veronica Kiian
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Anna Kloska
- The Faculty of Medicine, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Joanna Janiszewska-Olszowska
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, Al. Powstańców Wlkp. 72, 70-111 Szczecin, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
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Altındağ A, Bahrilli S, Çelik Ö, Bayrakdar İŞ, Orhan K. The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study. Diagnostics (Basel) 2024; 14:890. [PMID: 38732305 PMCID: PMC11082950 DOI: 10.3390/diagnostics14090890] [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: 03/01/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training (n = 1929, 80% of the total), validation (n = 240, 10% of the total), and test (n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.
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Affiliation(s)
- Ali Altındağ
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, 42090 Konya, Turkey;
| | - Serkan Bahrilli
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, 42090 Konya, Turkey;
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskişehir Osmangazi University, 26480 Eskişehir, Turkey;
| | - İbrahim Şevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, 26040 Eskişehir, Turkey;
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, 06560 Ankara, Turkey;
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Park JA, Kim D, Yang S, Kang JH, Kim JE, Huh KH, Lee SS, Yi WJ, Heo MS. Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network. Dentomaxillofac Radiol 2024; 53:22-31. [PMID: 38214942 PMCID: PMC11003607 DOI: 10.1093/dmfr/twad002] [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: 07/24/2023] [Revised: 09/15/2023] [Accepted: 10/18/2023] [Indexed: 01/13/2024] Open
Abstract
OBJECTIVES This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. METHODS PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). RESULTS The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. CONCLUSIONS This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.
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Affiliation(s)
- Jae-An Park
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - DaEl Kim
- Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Su Yang
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Ju-Hee Kang
- Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
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Putra RH, Astuti ER, Nurrachman AS, Putri DK, Ghazali AB, Pradini TA, Prabaningtyas DT. Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review. Imaging Sci Dent 2023; 53:271-281. [PMID: 38174035 PMCID: PMC10761295 DOI: 10.5624/isd.20230058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/14/2023] [Accepted: 07/14/2023] [Indexed: 01/05/2024] Open
Abstract
Purpose The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Material and Methods An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.
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Affiliation(s)
- Ramadhan Hardani Putra
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Eha Renwi Astuti
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Aga Satria Nurrachman
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Dina Karimah Putri
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
- Division of Dental Informatics and Radiology, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Ahmad Badruddin Ghazali
- Oral Radiology Unit, Department of Oral Maxillofacial Surgery and Oral Diagnosis, Kulliyyah of Dentistry, International Islamic University Malaysia, Malaysia
| | - Tjio Andrinanti Pradini
- Undergraduate Program, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
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11
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Singhal I, Kaur G, Neefs D, Pathak A. A Literature Review of the Future of Oral Medicine and Radiology, Oral Pathology, and Oral Surgery in the Hands of Technology. Cureus 2023; 15:e45804. [PMID: 37876387 PMCID: PMC10591112 DOI: 10.7759/cureus.45804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2023] [Indexed: 10/26/2023] Open
Abstract
In the realm of dentistry, a myriad of technological advancements, including teledentistry, virtual reality (VR), artificial intelligence (AI), and three-dimensional printing, have been extensively embraced and rigorously evaluated, consistently demonstrating their remarkable effectiveness. These innovations have ushered in a transformative era in dentistry, impacting every facet of the field. They encompass activities ranging from the diagnosis and exploration of oral health conditions to the formulation of treatment plans, execution of surgical procedures, fabrication of prosthetics, and even assistance in patient distraction, prognosis, and disease prevention. Despite the significant strides already taken, the relentless pursuit of new horizons fueled by human curiosity remains unabated. The future landscape of dentistry holds the promise of sweeping changes, notably characterized by enhanced accessibility to dental care and reduced treatment durations. In this comprehensive review article, we delve into the pivotal roles played by AI, VR, augmented reality, mixed reality, and extended reality within the realm of dentistry, with a particular emphasis on their applications in oral medicine, oral radiology, oral surgery, and oral pathology. These technologies represent just a fraction of the technological arsenal currently harnessed in the field of dentistry. A thorough comprehension of their advantages and limitations is imperative for informed decision-making in their utilization.
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Affiliation(s)
- Ishita Singhal
- Oral Pathology and Microbiology and Forensic Odontology, Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram, IND
| | - Geetpriya Kaur
- Oral Pathology and Microbiology, Paradise Diagnostics, New Delhi, IND
| | - Dirk Neefs
- Dentistry, Dierick Dental Care, Antwerp, BEL
| | - Aparna Pathak
- Oral Pathology, Paradise Diagnostics, New Delhi, IND
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12
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Anil S, Porwal P, Porwal A. Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques. Cureus 2023; 15:e41694. [PMID: 37575741 PMCID: PMC10413921 DOI: 10.7759/cureus.41694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
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Affiliation(s)
| | - Priyanka Porwal
- Dentistry, Pushpagiri Institute of Medical Sciences and Research Centre, Tiruvalla, IND
| | - Amit Porwal
- Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan, SAU
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13
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Artificial Intelligence (AI) for Detection and Localization of Unobturated Second Mesial Buccal (MB2) Canals in Cone-Beam Computed Tomography (CBCT). Diagnostics (Basel) 2022; 12:diagnostics12123214. [PMID: 36553221 PMCID: PMC9777585 DOI: 10.3390/diagnostics12123214] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/13/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The aim of this study was to develop a deep learning model to automatically detect and segment unobturated mesial buccal 2 (MB2) canals on endodontically obturated maxillary molars depicted in CBCT studies. Fifty-seven deidentified CBCT studies of maxillary molars with clinically confirmed unobturated MB2 canals were retrieved from a dental institution radiology database. One-hundred and two maxillary molar roots with and without unobturated MB2 canals were segmented using ITK-SNAP. The data were split into training and testing samples designated to train and evaluate the performance, respectively, of a convolutional neural network (CNN), U-Net. The detection performance revealed a sensitivity of 0.8, a specificity of 1, a high PPV of 1, and a NPV of 0.83 for the testing set, along with an accuracy of 0.9. The segmentation performance of unobturated MB2 canals, assessed using the custom metric, rendered a mean value of 0.3018 for the testing set. The current AI algorithm has the potential to identify obturated and unobturated canals in endodontically treated teeth. However, the AI algorithm is still somewhat affected by metallic artifacts, variations in canal calcifications, and the applied configuration. Thus, further development is needed to improve the algorithm and validate the accuracy using external validation data sets.
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14
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Ngnamsie Njimbouom S, Lee K, Kim JD. MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10928. [PMID: 36078635 PMCID: PMC9518085 DOI: 10.3390/ijerph191710928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases. The large amount of online data allows scholars to perform tremendous research on health conditions, specifically oral health. Regardless of the high-performing dental consultation tools available in current healthcare, computer-based technology has shown the ability to complete some tasks in less time and cost less than when using similar healthcare tools to perform the same type of work. Machine learning has displayed a wide variety of advantages in oral healthcare, such as predicting dental caries in the population. Compared to the standard dental caries prediction previously proposed, this work emphasizes the importance of using multiple data sources, referred to as multi-modality, to extract more features and obtain accurate performances. The proposed prediction model constructed using multi-modal data demonstrated promising performances with an accuracy of 90%, F1-score of 89%, a recall of 90%, and a precision of 89%.
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Affiliation(s)
| | - Kwonwoo Lee
- Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan 31460, Korea
| | - Jeong-Dong Kim
- Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan 31460, Korea
- Genome-Based BioIT Convergence Institute, Sun Moon University, Asan 31460, Korea
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15
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DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. Classified as one of the most prevalent oral health issues, research on dental caries has been carried out for early detection due to pain and cost of treatment. Medical research in oral healthcare has shown limitations such as considerable funds and time required; therefore, artificial intelligence has been used in recent years to develop models that can predict the risk of dental caries. The data used in our study were collected from a children’s oral health survey conducted in 2018 by the Korean Center for Disease Control and Prevention. Several Machine Learning algorithms were applied to this data, and their performances were evaluated using accuracy, F1-score, precision, and recall. Random forest has achieved the highest performance compared to other machine learnings methods, with an accuracy of 92%, F1-score of 90%, precision of 94%, and recall of 87%. The results of the proposed paper show that ML is highly recommended for dental professionals in assisting them in decision making for the early detection and treatment of dental caries.
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