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Garcovich D, Lipani E, Aiuto R, Alvarado Lorenzo A, Adobes Martin M. Application of digital workflow and technologies in clinical paediatric dentistry: a scoping review. Eur Arch Paediatr Dent 2024:10.1007/s40368-024-00936-0. [PMID: 39222211 DOI: 10.1007/s40368-024-00936-0] [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: 06/02/2024] [Accepted: 07/24/2024] [Indexed: 09/04/2024]
Abstract
PURPOSE The purpose of the present scoping review is to map the literature reporting on the application of digital workflows and digital technologies in the diagnosis, treatment, or management of dental conditions in paediatric patients. Furthermore, the review focuses on identifying possible knowledge gaps in the area and developing specific recommendations for future investigations. METHODS An electronic search was performed on 3 databases up to July 2023. After the authors independently screened the retrieved articles, they extracted the data and assessed the risk of bias using the JBI (The Joanna Briggs Institute) critical appraisal tools and the Cochrane Risk of Bias 1 tool, depending on the study design assessed. RESULTS After full-text assessment, 58 studies were identified that met the inclusion and exclusion criteria. The results were divided into two groups according to the study design: 36 were research articles, and 22 were case reports; only the research articles were included in the qualitative synthesis. The most common topic was Scanners/3 d digital model analysis (11 articles), followed by Digital Imaging (8 articles). Digital applications were also a popular topic, and tele-dentistry and artificial intelligence were also present in the included studies. CONCLUSION Studies investigating the use of digital workflows and digital technologies in the diagnosis, treatment or management of dental conditions in paediatric dentistry are lacking. In general, future investigations should be based on higher quality studies; furthermore, the lack of studies on the clinical validation of digitally fabricated orthodontic devices and restorations in paediatric patients provides insights for future research.
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Affiliation(s)
- Daniele Garcovich
- Department of Dentistry, European University of Valencia, Paseo de La Alameda 7, 46010, Valencia, Spain.
| | - Erica Lipani
- Department of Surgical Sciences, University of Cagliari, Gagliari, Italy
| | - Riccardo Aiuto
- Department of Biomedical Sciences, University of Milan, Milan, Italy
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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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Okazaki S, Mine Y, Yoshimi Y, Iwamoto Y, Ito S, Peng TY, Nishimura T, Suehiro T, Koizumi Y, Nomura R, Tanimoto K, Kakimoto N, Murayama T. RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01204-9. [PMID: 39048809 DOI: 10.1007/s10278-024-01204-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different problems. ImageNet, which is a publicly available large-scale dataset, is a commonly used dataset for TL-based image analysis; many studies have applied pre-trained models from ImageNet to clinical prediction tasks and have reported promising results. However, some have questioned the effectiveness of using ImageNet, which consists solely of natural images, for medical image analysis. The aim of this study was to evaluate whether pre-trained models using RadImageNet, which is a large-scale medical image dataset, could achieve superior performance in classification tasks in dental imaging modalities compared with ImageNet pre-trained models. To evaluate the classification performance of RadImageNet and ImageNet pre-trained models for TL, two dental imaging datasets were used. The tasks were (1) classifying the presence or absence of supernumerary teeth from a dataset of panoramic radiographs and (2) classifying sex from a dataset of lateral cephalometric radiographs. Performance was evaluated by comparing the area under the curve (AUC). On the panoramic radiograph dataset, the RadImageNet models gave average AUCs of 0.68 ± 0.15 (p < 0.01), and the ImageNet models had values of 0.74 ± 0.19. In contrast, on the lateral cephalometric dataset, the RadImageNet models demonstrated average AUCs of 0.76 ± 0.09, and the ImageNet models achieved values of 0.75 ± 0.17. The difference in performance between RadImageNet and ImageNet models in TL depends on the dental image dataset used.
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Affiliation(s)
- Shota Okazaki
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
- Project Research Center for Integrating Digital Dentistry, Hiroshima University, Hiroshima, Japan
| | - Yuichi Mine
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
- Project Research Center for Integrating Digital Dentistry, Hiroshima University, Hiroshima, Japan.
| | - Yuki Yoshimi
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuko Iwamoto
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shota Ito
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tzu-Yu Peng
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Taku Nishimura
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tomoya Suehiro
- Department of Genomic Oncology and Oral Medicine, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan
| | - Yuma Koizumi
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ryota Nomura
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kotaro Tanimoto
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takeshi Murayama
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
- Project Research Center for Integrating Digital Dentistry, Hiroshima University, Hiroshima, Japan.
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Hayashi-Sakai S, Nishiyama H, Hayashi T, Sakai J, Shimomura-Kuroki J. Deep learning with convolution neural network detecting mesiodens on panoramic radiographs: comparing four models. Odontology 2024:10.1007/s10266-024-00980-8. [PMID: 39017730 DOI: 10.1007/s10266-024-00980-8] [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: 03/18/2024] [Accepted: 07/05/2024] [Indexed: 07/18/2024]
Abstract
The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiodens were used as training, validation, and test data. The training, validation, and test dataset were consisted of 218, 51, and 40 images with mesiodens and 203, 55, and 61 without mesiodens, respectively. Unclear panoramic radiographs for which the diagnosis could not be accurately determined and other modalities were required for the final diagnosis were retrospectively identified and employed as the training dataset. Four CNN models provided within software supporting the creation of neural network models for deep learning were modified and developed. The diagnostic performance of the CNNs was evaluated according to accuracy, precision, recall and F1 scores, receiver operating characteristics (ROC) curves, and area under the ROC curve (AUC). In addition, we used SHapley Additive exPlanations (SHAP) to attempt to visualize the image features that were important in the classifications of the model that exhibited the best diagnostic performance. A binary_connect_mnist_LeNet model exhibited the best performance of the four deep learning models. Our results suggest that a simple lightweight model is able to detect mesiodens. It is worth referring to AI-based diagnosis before an additional radiological examination when diagnosis of mesiodens cannot be made on unclear images. However, further revaluation by the specialist would be also necessary for careful consideration because children are more radiosensitive than adults.
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Affiliation(s)
- Sachiko Hayashi-Sakai
- Department of Pediatric Dentistry, The Nippon Dental University School of Life Dentistry at Niigata, 1-8 Hamaura-cho, Chuo-ku, Niigata, 951-8580, Japan.
| | - Hideyoshi Nishiyama
- Division of Oral and Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Niigata University, 2-5274 Gakkocho-dori, Chuo-ku, Niigata, 951-8514, Japan
| | - Takafumi Hayashi
- Division of Oral and Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Niigata University, 2-5274 Gakkocho-dori, Chuo-ku, Niigata, 951-8514, Japan
| | - Jun Sakai
- Department of System and Automotive Engineering, Niigata College of Technology, 5-13-7 Kamishinei-cho, Nishi-ku, Niigata, 950-2076, Japan
| | - Junko Shimomura-Kuroki
- Department of Pediatric Dentistry, The Nippon Dental University School of Life Dentistry at Niigata, 1-8 Hamaura-cho, Chuo-ku, Niigata, 951-8580, Japan
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Beser B, Reis T, Berber MN, Topaloglu E, Gungor E, Kılıc MC, Duman S, Çelik Ö, Kuran A, Bayrakdar IS. YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition. BMC Med Imaging 2024; 24:172. [PMID: 38992601 PMCID: PMC11238494 DOI: 10.1186/s12880-024-01338-w] [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: 04/13/2024] [Accepted: 06/17/2024] [Indexed: 07/13/2024] Open
Abstract
OBJECTIVES In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. METHODS A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. RESULTS The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. CONCLUSIONS YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.
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Affiliation(s)
- Busra Beser
- Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
| | - Tugba Reis
- Pedodontics, Private Practice, Trabzon, Turkey
| | - Merve Nur Berber
- Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
| | - Edanur Topaloglu
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Esra Gungor
- Department of Pedodontics, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Münevver Coruh Kılıc
- Department of Pedodontics, Faculty of Dentistry, Beykent University, Istanbul, Turkey
| | - Sacide Duman
- Department of Pedodontics, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alican Kuran
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, İzmit, Kocaeli, 41190, Turkey.
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
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Yoshimi Y, Mine Y, Ito S, Takeda S, Okazaki S, Nakamoto T, Nagasaki T, Kakimoto N, Murayama T, Tanimoto K. Image preprocessing with contrast-limited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the temporomandibular joint on magnetic resonance images. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:128-141. [PMID: 37263812 DOI: 10.1016/j.oooo.2023.01.016] [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: 10/22/2022] [Revised: 01/11/2023] [Accepted: 01/21/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVES The objective was to evaluate the robustness of deep learning (DL)-based encoder-decoder convolutional neural networks (ED-CNNs) for segmenting temporomandibular joint (TMJ) articular disks using data sets acquired from 2 different 3.0-T magnetic resonance imaging (MRI) scanners using original images and images subjected to contrast-limited adaptive histogram equalization (CLAHE). STUDY DESIGN In total, 536 MR images from 49 individuals were examined. An expert orthodontist identified and manually segmented the disks in all images, which were then reviewed by another expert orthodontist and 2 expert oral and maxillofacial radiologists. These images were used to evaluate a DL-based semantic segmentation approach using an ED-CNN. Original and preprocessed CLAHE images were used to train and validate the models whose performances were compared. RESULTS Original and CLAHE images acquired on 1 scanner had pixel values that were significantly darker and with lower contrast. The values of 3 metrics-the Dice similarity coefficient, sensitivity, and positive predictive value-were low when the original MR images were used for model training and validation. However, these metrics significantly improved when images were preprocessed with CLAHE. CONCLUSIONS The robustness of the ED-CNN model trained on a dataset obtained from a single device is low but can be improved with CLAHE preprocessing. The proposed system provides promising results for a DL-based, fully automated segmentation method for TMJ articular disks on MRI.
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Affiliation(s)
- Yuki Yoshimi
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuichi Mine
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
| | - Shota Ito
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Saori Takeda
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Shota Okazaki
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takashi Nakamoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Toshikazu Nagasaki
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Takeshi Murayama
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kotaro Tanimoto
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [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: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Li W, Wang Y, Liu Y. DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs. Dentomaxillofac Radiol 2024; 53:296-307. [PMID: 38518093 PMCID: PMC11211679 DOI: 10.1093/dmfr/twae014] [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: 12/27/2023] [Revised: 03/03/2024] [Accepted: 03/19/2024] [Indexed: 03/24/2024] Open
Abstract
OBJECTIVES Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems. METHODS We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria. RESULTS About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively. CONCLUSIONS The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.
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Affiliation(s)
- Wei Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yu Liu
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
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Hase H, Mine Y, Okazaki S, Yoshimi Y, Ito S, Peng TY, Sano M, Koizumi Y, Kakimoto N, Tanimoto K, Murayama T. Sex estimation from maxillofacial radiographs using a deep learning approach. Dent Mater J 2024; 43:394-399. [PMID: 38599831 DOI: 10.4012/dmj.2023-253] [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] [Indexed: 04/12/2024]
Abstract
The purpose of this study was to construct deep learning models for more efficient and reliable sex estimation. Two deep learning models, VGG16 and DenseNet-121, were used in this retrospective study. In total, 600 lateral cephalograms were analyzed. A saliency map was generated by gradient-weighted class activation mapping for each output. The two deep learning models achieved high values in each performance metric according to accuracy, sensitivity (recall), precision, F1 score, and areas under the receiver operating characteristic curve. Both models showed substantial differences in the positions indicated in saliency maps for male and female images. The positions in saliency maps also differed between VGG16 and DenseNet-121, regardless of sex. This analysis of our proposed system suggested that sex estimation from lateral cephalograms can be achieved with high accuracy using deep learning.
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Affiliation(s)
- Hiroki Hase
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuichi Mine
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
- Project Research Center for Integrating Digital Dentistry, Hiroshima University
| | - Shota Okazaki
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
- Project Research Center for Integrating Digital Dentistry, Hiroshima University
| | - Yuki Yoshimi
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Shota Ito
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Tzu-Yu Peng
- School of Dentistry, College of Oral Medicine, Taipei Medical University
| | - Mizuho Sano
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuma Koizumi
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Naoya Kakimoto
- School of Dentistry, College of Oral Medicine, Taipei Medical University
| | - Kotaro Tanimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Takeshi Murayama
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
- Project Research Center for Integrating Digital Dentistry, Hiroshima University
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10
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Otsuka Y, Indo H, Kawashima Y, Tanaka T, Kono H, Kikuchi M. Eichner classification based on panoramic X-ray images using deep learning: A pilot study. Biomed Mater Eng 2024; 35:377-386. [PMID: 38848165 DOI: 10.3233/bme-230217] [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] [Indexed: 06/09/2024]
Abstract
BACKGROUND Research using panoramic X-ray images using deep learning has been progressing in recent years. There is a need to propose methods that can classify and predict from image information. OBJECTIVE In this study, Eichner classification was performed on image processing based on panoramic X-ray images. The Eichner classification was based on the remaining teeth, with the aim of making partial dentures. This classification was based on the condition that the occlusal position was supported by the remaining teeth in the upper and lower jaws. METHODS Classification models were constructed using two convolutional neural network methods: the sequential and VGG19 models. The accuracy was compared with the accuracy of Eichner classification using the sequential and VGG19 models. RESULTS Both accuracies were greater than 81%, and they had sufficient functions for the Eichner classification. CONCLUSION We were able to build a highly accurate prediction model using deep learning scratch sequential model and VGG19. This predictive model will become part of the basic considerations for future AI research in dentistry.
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Affiliation(s)
- Yuta Otsuka
- Department of Biomaterials Science, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Hiroko Indo
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Yusuke Kawashima
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Tatsuro Tanaka
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Hiroshi Kono
- Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Masafumi Kikuchi
- Department of Biomaterials Science, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
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11
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Surlari Z, Budală DG, Lupu CI, Stelea CG, Butnaru OM, Luchian I. Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry-A Narrative Review. J Clin Med 2023; 12:7378. [PMID: 38068430 PMCID: PMC10707023 DOI: 10.3390/jcm12237378] [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] [Received: 11/05/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 07/25/2024] Open
Abstract
The concept of machines learning and acting like humans is what is meant by the phrase "artificial intelligence" (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.
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Affiliation(s)
- Zinovia Surlari
- Department of Fixed Protheses, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Dana Gabriela Budală
- Department of Implantology, Removable Prostheses, Dental Prostheses Technology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Costin Iulian Lupu
- Department of Dental Management, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Carmen Gabriela Stelea
- Department of Oral Surgery, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Oana Maria Butnaru
- Department of Biophysics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 Universității Street, 700115 Iasi, Romania;
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12
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Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review. Diagnostics (Basel) 2023; 13:2512. [PMID: 37568875 PMCID: PMC10416832 DOI: 10.3390/diagnostics13152512] [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: 07/11/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.
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Affiliation(s)
- Esra Sivari
- Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey
| | | | - Erkan Bostanci
- Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
| | | | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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Vishwanathaiah S, Fageeh HN, Khanagar SB, Maganur PC. Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review. Biomedicines 2023; 11:biomedicines11030788. [PMID: 36979767 PMCID: PMC10044793 DOI: 10.3390/biomedicines11030788] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023] Open
Abstract
In the global epidemic era, oral problems significantly impact a major population of children. The key to a child’s optimal health is early diagnosis, prevention, and treatment of these disorders. In recent years, the field of artificial intelligence (AI) has seen tremendous pace and progress. As a result, AI’s infiltration is witnessed even in those areas that were traditionally thought to be best left to human specialists. The ultimate ability to improve patient care and make precise diagnoses of illnesses has revolutionized the world of healthcare. In the field of dentistry, the competence to execute treatment measures while still providing appropriate patient behavior counseling is in high demand, particularly in the field of pediatric dental care. As a result, we decided to conduct this review specifically to examine the applications of AI models in pediatric dentistry. A comprehensive search of the subjects was done using a wide range of databases to look for studies that have been published in peer-reviewed journals from its inception until 31 December 2022. After the application of the criteria, only 25 of the 351 articles were taken into consideration for this review. According to the literature, AI is frequently used in pediatric dentistry for the purpose of making an accurate diagnosis and assisting clinicians, dentists, and pediatric dentists in clinical decision making, developing preventive strategies, and establishing an appropriate treatment plan.
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Affiliation(s)
- Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Division of Pediatric Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence: (S.V.); (P.C.M.); Tel.: +966-542635434 (S.V.); +966-505916621 (P.C.M.)
| | - Hytham N. Fageeh
- Department of Preventive Dental Sciences, Division of Periodontics, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
| | - Sanjeev B. Khanagar
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
- King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh 11481, Saudi Arabia
| | - Prabhadevi C. Maganur
- Department of Preventive Dental Sciences, Division of Pediatric Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence: (S.V.); (P.C.M.); Tel.: +966-542635434 (S.V.); +966-505916621 (P.C.M.)
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Dai X, Jiang X, Jing Q, Zheng J, Zhu S, Mao T, Wang D. A one-stage deep learning method for fully automated mesiodens localization on panoramic radiographs. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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15
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Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm. Oral Radiol 2023; 39:207-214. [PMID: 35612677 DOI: 10.1007/s11282-022-00622-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/30/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVES Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography. METHODS 434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model. Datasets were split into train, validation, and test groups of both normal and masked images. The data augmentation method was applied to images of trainings and validation groups with vertical flip images, horizontal flip images, and both flip images. The Confusion Matrix was used to determine the model performance. RESULTS Among the 43 test group images with 126 labels, there were 109 true positives, 29 false positives, and 17 false negatives. The sensitivity, precision, and F1-score values of taurodont tooth segmentation were 0.8650, 0.7898, and 0.8257, respectively. CONCLUSIONS CNN's ability to identify taurodontism produced almost identical results to the labeled training data, and the CNN system achieved close to the expert level results in its ability to detect the taurodontism of teeth.
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Okazaki S, Mine Y, Iwamoto Y, Urabe S, Mitsuhata C, Nomura R, Kakimoto N, Murayama T. Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs. Dent Mater J 2022; 41:889-895. [PMID: 36002296 DOI: 10.4012/dmj.2022-098] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The aim of the feasibility study was to construct deep learning models for the classification of multiple dental anomalies in panoramic radiographs. Panoramic radiographs with single supernumerary teeth and/or odontomas were considered the "case" group; panoramic radiographs with no dental anomalies were considered the "control" group. The dataset comprised 150 panoramic radiographs: 50 each of no dental anomalies, single supernumerary teeth, and odontomas. To classify the panoramic radiographs into case and control categories, we employed AlexNet, which is a convolutional neural network model. AlexNet was able to classify whole panoramic radiographs into two or three classes, according to the presence or absence of supernumerary teeth or odontomas. The performance metrics of the three-class classification were 70%, 70.8%, 70%, and 69.7% for accuracy, precision, sensitivity, and F1 score, respectively, in the macro average. These results support the feasibility of using deep learning to detect multiple dental anomalies in panoramic radiographs.
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Affiliation(s)
- Shota Okazaki
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuichi Mine
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuko Iwamoto
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Shiho Urabe
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Chieko Mitsuhata
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Ryota Nomura
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Takeshi Murayama
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
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