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Diagnostics
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Artificial Intelligence in the Diagnostics of Dental Disease
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Artificial Intelligence in the Diagnostics of Dental Disease

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 July 2024) | Viewed by 15107

Special Issue Editor

Dr. Hakan Turkkahraman

E-Mail Website
Guest Editor
Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
Interests: artificial intelligence; machine learning; clinical decision support systems; orthodontic diagnosis and treatment planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am honoured to invite you to submit your valuable studies to a Special Issue of Diagnostics on “Artificial Intelligence in the Diagnostics of Dental Disease”. Recent developments in artificial intelligence and machine learning have created infinite new opportunities for humanity in almost all aspects of life. However, the sudden surge in AI applications has created a cacophony of supporters and detractors, making it difficult to differentiate between the advantages and disadvantages of such systems, as well as their associated risks. Nevertheless, AI is here to stay, and it is up to scientists to determine the best practices for using it while minimizing risks. Therefore, in every discipline, those who have ethical responsibility should conduct high-quality scientific research.

AI has the potential to significantly improve the accuracy and speed of dental disease diagnosis, leading to earlier interventions and better outcomes for patients. Among many applications, AI can analyse images from dental scans, X-rays, and other imaging techniques to identify signs of dental diseases, such as cavities, fractures, or periodontal diseases. Moreover, AI can recognize patterns of dental diseases in large datasets, identifying commonalities between patients with similar dental conditions. AI can also analyse a patient's dental health history and use these data to predict their likelihood of developing certain dental conditions in the future. AI can provide decision support to dental professionals, offering recommendations for treatment plans based on patient data and best practices. I would like to take this opportunity to invite you to contribute to the growing field of AI in the diagnosis of dental diseases.

Dr. Hakan Turkkahraman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • neural networks
  • dental diseases
  • clinical decision support systems
  • medical diagnosis

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Published Papers (6 papers)

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Research

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13 pages, 2111 KiB  
Article
A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs
by Jing Hao, Lun M. Wong, Zhiyi Shan, Qi Yong H. Ai, Xieqi Shi, James Kit Hon Tsoi and Kuo Feng Hung
Diagnostics 2024, 14(17), 1948; https://doi.org/10.3390/diagnostics14171948 - 3 Sep 2024
Viewed by 1030
Abstract
Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework [...] Read more.
Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework (SemiTNet), specifically designed to improve tooth segmentation and identification performance on panoramic radiographs, particularly in partially edentulous cases, and establish an open-source dataset to serve as a unified benchmark. A total of 16,317 panoramic radiographs (1589 labeled and 14,728 unlabeled images) were collected from various datasets to create a large-scale dataset (TSI15k). The labeled images were divided into training and test sets at a 7:1 ratio, while the unlabeled images were used for semi-supervised learning. The SemiTNet was developed using a semi-supervised learning method with a label-guided teacher–student knowledge distillation strategy, incorporating a Transformer-based architecture. The performance of SemiTNet was evaluated on the test set using the intersection over union (IoU), Dice coefficient, precision, recall, and F1 score, and compared with five state-of-the-art networks. Paired t-tests were performed to compare the evaluation metrics between SemiTNet and the other networks. SemiTNet outperformed other networks, achieving the highest accuracy for tooth segmentation and identification, while requiring minimal model size. SemiTNet’s performance was near-perfect for fully dentate individuals (all metrics over 99.69%) and excellent for partially edentulous individuals (all metrics over 93%). In edentulous cases, SemiTNet obtained statistically significantly higher tooth identification performance than all other networks. The proposed SemiTNet outperformed previous high-complexity, state-of-the-art networks, particularly in partially edentulous cases. The established open-source TSI15k dataset could serve as a unified benchmark for future studies. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Disease)
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18 pages, 10331 KiB  
Article
Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs
by Tai-Jung Lin, Yi-Cheng Mao, Yuan-Jin Lin, Chin-Hao Liang, Yi-Qing He, Yun-Chen Hsu, Shih-Lun Chen, Tsung-Yi Chen, Chiung-An Chen, Kuo-Chen Li and Patricia Angela R. Abu
Diagnostics 2024, 14(15), 1687; https://doi.org/10.3390/diagnostics14151687 - 4 Aug 2024
Viewed by 1355
Abstract
The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth’s bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to [...] Read more.
The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth’s bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Disease)
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11 pages, 2380 KiB  
Article
Artificial Intelligence for 3D Reconstruction from 2D Panoramic X-rays to Assess Maxillary Impacted Canines
by Sumeet Minhas, Tai-Hsien Wu, Do-Gyoon Kim, Si Chen, Yi-Chu Wu and Ching-Chang Ko
Diagnostics 2024, 14(2), 196; https://doi.org/10.3390/diagnostics14020196 - 16 Jan 2024
Cited by 1 | Viewed by 2203
Abstract
The objective of this study was to explore the feasibility of current 3D reconstruction in assessing the position of maxillary impacted canines from 2D panoramic X-rays. A dataset was created using pre-treatment CBCT data from a total of 123 patients, comprising 74 patients [...] Read more.
The objective of this study was to explore the feasibility of current 3D reconstruction in assessing the position of maxillary impacted canines from 2D panoramic X-rays. A dataset was created using pre-treatment CBCT data from a total of 123 patients, comprising 74 patients with impacted canines and 49 patients without impacted canines. From all 74 subjects, we generated a dataset containing paired 2D panoramic X-rays and pseudo-3D images. This pseudo-3D image contained information about the location of the impacted canine in the buccal/lingual, mesial/distal, and apical/coronal positions. These data were utilized to train a deep-learning reconstruction algorithm, a generative AI. The location of the crown of the maxillary impacted canine was determined based on the output of the algorithm. The reconstruction was evaluated using the structure similarity index measure (SSIM) as a metric to indicate the quality of the reconstruction. The prediction of the impacted canine’s location was assessed in both the mesiodistal and buccolingual directions. The reconstruction algorithm predicts the position of the impacted canine in the buccal, middle, or lingual position with 41% accuracy, while the mesial and distal positions are predicted with 55% accuracy. The mean SSIM for the output is 0.71, with a range of 0.63 to 0.84. Our study represents the first application of AI reconstruction output for multidisciplinary care involving orthodontists, periodontists, and maxillofacial surgeons in diagnosing and treating maxillary impacted canines. Further development of deep-learning algorithms is necessary to enhance the robustness of dental reconstruction applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Disease)
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15 pages, 1652 KiB  
Article
Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate
by Mohamed Zahoor Ul Huqh, Johari Yap Abdullah, Matheel AL-Rawas, Adam Husein, Wan Muhamad Amir W Ahmad, Nafij Bin Jamayet, Maya Genisa and Mohd Rosli Bin Yahya
Diagnostics 2023, 13(19), 3025; https://doi.org/10.3390/diagnostics13193025 - 22 Sep 2023
Viewed by 1490
Abstract
Introduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression [...] Read more.
Introduction: Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals. Methods: This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen. Results: The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study’s output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%. Conclusion: The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Disease)
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16 pages, 7329 KiB  
Article
Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning
by Matthew Parrish, Ella O’Connell, George Eckert, Jay Hughes, Sarkhan Badirli and Hakan Turkkahraman
Diagnostics 2023, 13(17), 2729; https://doi.org/10.3390/diagnostics13172729 - 22 Aug 2023
Cited by 2 | Viewed by 1185
Abstract
The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML [...] Read more.
The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Disease)
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Other

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28 pages, 4829 KiB  
Systematic Review
Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review
by Esra Sivari, Guler Burcu Senirkentli, Erkan Bostanci, Mehmet Serdar Guzel, Koray Acici and Tunc Asuroglu
Diagnostics 2023, 13(15), 2512; https://doi.org/10.3390/diagnostics13152512 - 27 Jul 2023
Cited by 12 | Viewed by 6887
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Disease)
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