Artificial Intelligence in Dental Medicine

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 12994

Special Issue Editors


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Guest Editor
Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA 02115, USA
Interests: oral medicine; infection and immunity

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Guest Editor
Department of Restorative Dentistry and Biomaterials Science, Harvard School of Dental Medicine, Boston, MA 02115, USA
Interests: clinical and translational research in implant dentistry; applied digital dental technology including computer guided implant surgery; digital impression; CAD-CAM implant rehabilitations

E-Mail Website
Guest Editor
Department of Restorative Dentistry and Biomaterials Science, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
Interests: the application of color science in dentistry; bone growth/regeneration; public health issues

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has received enormous attention and has transitioned from being a purely statistical tool to being one of the main drivers of modern medicine. Every field has enthusiastically opted for artificial intelligence, and the field of dental medicine is no exception. AI represents an effective approach for analyzing clinical dental data. The Special Issue aims to highlight the recent progress and trends in utilizing AI techniques, such as machine learning and deep learning, for detecting, screening, diagnosing, and monitoring numerous dental diseases and treatments in diverse clinical practices and in the basic research of dental medicine.

Dr. Shigemi Ishikawa-Nagai
Dr. Sang Jin Lee
Dr. John D. Da Silva
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • dental medicine

Published Papers (6 papers)

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31 pages, 2772 KiB  
Article
Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease
by Ovidiu Boitor, Florin Stoica, Romeo Mihăilă, Laura Florentina Stoica and Laura Stef
Diagnostics 2023, 13(24), 3631; https://doi.org/10.3390/diagnostics13243631 - 08 Dec 2023
Viewed by 821
Abstract
Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition [...] Read more.
Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45–79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dental Medicine)
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11 pages, 933 KiB  
Article
Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks
by Helena Dujic, Ole Meyer, Patrick Hoss, Uta Christine Wölfle, Annika Wülk, Theresa Meusburger, Leon Meier, Volker Gruhn, Marc Hesenius, Reinhard Hickel and Jan Kühnisch
Diagnostics 2023, 13(23), 3562; https://doi.org/10.3390/diagnostics13233562 - 29 Nov 2023
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Abstract
Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to [...] Read more.
Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1–96.7%; AUC 0.944–0.970) and upper anterior (86.7–90.2%; 0.948–0.958) and lower (85.6–87.2%; 0.913–0.937) and upper posterior teeth (78.1–81.0%; 0.851–0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dental Medicine)
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12 pages, 999 KiB  
Article
Tooth Type Enhanced Transformer for Children Caries Diagnosis on Dental Panoramic Radiographs
by Xiaojie Zhou, Guoxia Yu, Qiyue Yin, Jun Yang, Jiangyang Sun, Shengyi Lv and Qing Shi
Diagnostics 2023, 13(4), 689; https://doi.org/10.3390/diagnostics13040689 - 12 Feb 2023
Cited by 3 | Viewed by 1440
Abstract
The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely [...] Read more.
The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dental Medicine)
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13 pages, 4400 KiB  
Article
An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images
by Faruk Oztekin, Oguzhan Katar, Ferhat Sadak, Muhammed Yildirim, Hakan Cakar, Murat Aydogan, Zeynep Ozpolat, Tuba Talo Yildirim, Ozal Yildirim, Oliver Faust and U. Rajendra Acharya
Diagnostics 2023, 13(2), 226; https://doi.org/10.3390/diagnostics13020226 - 07 Jan 2023
Cited by 13 | Viewed by 5049
Abstract
Dental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people’s quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians [...] Read more.
Dental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people’s quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians frequently find the model results unsatisfactory due to a lack of explainability. Our study attempts to address this issue with an explainable deep learning model for detecting dental caries. We tested three prominent pre-trained models, EfficientNet-B0, DenseNet-121, and ResNet-50, to determine which is best for the caries detection task. These models take panoramic images as the input, producing a caries–non-caries classification result and a heat map, which visualizes areas of interest on the tooth. The model performance was evaluated using whole panoramic images of 562 subjects. All three models produced remarkably similar results. However, the ResNet-50 model exhibited a slightly better performance when compared to EfficientNet-B0 and DenseNet-121. This model obtained an accuracy of 92.00%, a sensitivity of 87.33%, and an F1-score of 91.61%. Visual inspection showed us that the heat maps were also located in the areas with caries. The proposed explainable deep learning model diagnosed dental caries with high accuracy and reliability. The heat maps help to explain the classification results by indicating a region of suspected caries on the teeth. Dentists could use these heat maps to validate the classification results and reduce misclassification. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dental Medicine)
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19 pages, 9315 KiB  
Article
GamaComet: A Deep Learning-Based Tool for the Detection and Classification of DNA Damage from Buccal Mucosa Comet Assay Images
by Afiahayati, Edgar Anarossi, Ryna Dwi Yanuaryska and Sri Mulyana
Diagnostics 2022, 12(8), 2002; https://doi.org/10.3390/diagnostics12082002 - 18 Aug 2022
Cited by 3 | Viewed by 1724
Abstract
Comet assay is a simple and precise method to analyze DNA damage. Nowadays, many research studies have demonstrated the effectiveness of buccal mucosa cells usage in comet assays. However, several software tools do not perform well for detecting and classifying comets from a [...] Read more.
Comet assay is a simple and precise method to analyze DNA damage. Nowadays, many research studies have demonstrated the effectiveness of buccal mucosa cells usage in comet assays. However, several software tools do not perform well for detecting and classifying comets from a comet assay image of buccal mucosa cells because the cell has a lot more noise. Therefore, a specific software tool is required for fully automated comet detection and classification from buccal mucosa cell swabs. This research proposes a deep learning-based fully automated framework using Faster R-CNN to detect and classify comets in a comet assay image taken from buccal mucosa swab. To train the Faster R-CNN model, buccal mucosa samples were collected from 24 patients in Indonesia. We acquired 275 comet assay images containing 519 comets. Furthermore, two strategies were used to overcome the lack of dataset problems during the model training, namely transfer learning and data augmentation. We implemented the proposed Faster R-CNN model as a web-based tool, GamaComet, that can be accessed freely for academic purposes. To test the GamaComet, buccal mucosa samples were collected from seven patients in Indonesia. We acquired 43 comet assay images containing 73 comets. GamaComet can give an accuracy of 81.34% for the detection task and an accuracy of 66.67% for the classification task. Furthermore, we also compared the performance of GamaComet with an existing free software tool for comet detection, OpenComet. The experiment results showed that GamaComet performed significantly better than OpenComet that could only give an accuracy of 11.5% for the comet detection task. Downstream analysis can be well conducted based on the detection and classification results from GamaComet. The analysis showed that patients owning comet assay images containing comets with class 3 and class 4 had a smoking habit, meaning they had more cells with a high level of DNA damage. Although GamaComet had a good performance, the performance for the classification task could still be improved. Therefore, it will be one of the future works for the research development of GamaComet. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dental Medicine)
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22 pages, 3367 KiB  
Systematic Review
Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review
by Gianna Dipalma, Alessio Danilo Inchingolo, Angelo Michele Inchingolo, Fabio Piras, Vincenzo Carpentiere, Grazia Garofoli, Daniela Azzollini, Merigrazia Campanelli, Gregorio Paduanelli, Andrea Palermo and Francesco Inchingolo
Diagnostics 2023, 13(24), 3677; https://doi.org/10.3390/diagnostics13243677 - 15 Dec 2023
Cited by 1 | Viewed by 1222
Abstract
This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software [...] Read more.
This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions. Materials and Methods: Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated. Results: A final number of 33 studies were included in the review for qualitative analysis. Conclusions: These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Dental Medicine)
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