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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Dentistry and Oral Sciences".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 24242

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Dr. Kathrin Becker

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Department of Orthodontics, University Hospital Dusseldorf, 40225 Dusseldorf, Germany
Interests: bone micro-morphometry; 3D imaging; dental radiology; orthodontic implants; skeletal anchorage
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has gained substantial public interest in recent years, and is considered to have great transformative potential in medicine and public health.

In dentistry, several studies have already highlighted various fields of application, including automated image segmentation, the detection of landmarks and/or pathologies on radiographs, assistance in treatment planning, the prediction of treatment success or failure, and the prognosis of skeletal growth.

This Special Issue aims to provide a comprehensive overview of the existing literature in the field of dentistry, to demonstrate the state-of the art application of AI in all disciplines, and to present an outlook on potential future developments and applications.

We cordially welcome manuscript on all aspects of AI applied to dentistry and oral health, focusing on orthodontics, periodontology, restorative dentistry, prosthodontics, implant dentistry, or oral and maxillofacial surgery.

Dr. Kathrin Becker
Guest Editor

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Keywords

anatomic landmark detection and/or analyses
automated treatment planning
prediction of treatment success, treatment failure and/or treatment duration
prognosis of growth and development
evaluation of treatment outcomes
image segmentation, object detection, object classification
detection of pathologies
big data
artificial neural networks
deep learning
machine learning
convolutional neural networks
fuzzy logic

Published Papers (9 papers)

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12 pages, 1330 KiB

Open AccessArticle

DeAPIR: Efficient and Enhanced Dental Arch-Guided Panoramic Image Reconstruction from CBCT Data

by Fawad and Seong-Yong Moon

Appl. Sci. 2023, 13(22), 12365; https://doi.org/10.3390/app132212365 - 15 Nov 2023

Viewed by 893

Abstract

Dental CBCT and panoramic imaging play a pivotal role in dental diagnosis and treatment planning, alongside the indispensable use of computed tomography (CT) and X-ray imaging in dentistry, particularly for surgical planning. Given the widespread adoption of dental cone beam CT in clinics today, we explore a novel approach in this study—utilizing CT’s three-dimensional (3D) data to reconstruct a two-dimensional (2D) panoramic dental image. This method eliminates the requirement for an extra panoramic scan. In this work, we propose a novel framework to generate an enhanced and extended 2D panoramic view by using the dental arch extracted from 3D CBCT. Our method involves segmenting the patient’s dental arch from their 3D CBCT image by identifying horizontal slices with above-average intensity, followed by morphological operations, including dilation, Gaussian filtering, and skeletonization, to delineate the dental arch line. Additionally, we extend the dental arch beyond the wisdom teeth using quadratic curve fitting. Finally, we employ Maximum Intensity Projection on rotated cubic segments aligned with the dental arch curve to produce captivating panoramic images. The panoramic view produced using our proposed method, when compared to the results obtained from BlueSky and OpenInventor, exhibited superior enhancements and greater accuracy in panoramic visualization. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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12 pages, 4788 KiB

Open AccessArticle

A Lightweight Knowledge-Distillation-Based Model for the Detection and Classification of Impacted Mandibular Third Molars

by Yujie Lei, Xiang Chen, Yunlong Wang, Rong Tang and Baoping Zhang

Appl. Sci. 2023, 13(17), 9970; https://doi.org/10.3390/app13179970 - 04 Sep 2023

Viewed by 1016

Abstract

The extraction of impacted third molars is one of the most common dental operations. When the impacted third molar is extracted, the operation plan is generally different because of the different impacted positions of the tooth. Therefore, judging the impacted type of the third molar is the basis of the third molar extraction operation. At present, oral health professionals usually analyze panoramic radiographs to determine the types of impacted third molars, but the diagnosis is easily affected by oral health professionals’ subjective consciousnesses. Computer vision technology can help doctors analyze medical images faster and more accurately, so it is very desirable to use computer vision to detect and classify the impacted third molars. Based on the panoramic radiographs of the School of Stomatology, Lanzhou University, this paper establishes an object detection dataset containing six types of impacted third molars. On the basis of this dataset, the lightweight third molar impacted detection and classification model is studied in this paper. This study introduces the method of knowledge distillation on the basis of YOLOv5s and uses YOLOv5x as the teacher’s model to guide YOLOv5s, which not only ensures the light weight of the model but also improves the accuracy of the model. Finally, the YOLOv5s-x model is obtained. The experimental results show that the introduction of knowledge distillation effectively improves the accuracy of the model while ensuring its light weight, the mAP of YOLOv5s-x is increased by 2.9% compared with the original model, and the amount of parameters and calculations is also reduced to a certain extent. Compared with mainstream object detection networks, including YOLOv8, YOLOv5s-x also has certain advantages, which can provide oral health professionals with better impacted third molar detection and classification services. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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10 pages, 3065 KiB

Open AccessArticle

Evaluating the Facial Esthetic Outcomes of Digital Smile Designs Generated by Artificial Intelligence and Dental Professionals

by Gülsüm Ceylan, Gülsüm Sayın Özel, Gözde Memişoglu, Faruk Emir and Sevgin Şen

Appl. Sci. 2023, 13(15), 9001; https://doi.org/10.3390/app13159001 - 06 Aug 2023

Cited by 2 | Viewed by 2243

Abstract

This study evaluates the preference rates for smile designs created by professionals or by Artificial Intelligence (AI) among dentists, dentistry students, and laypeople. Four cases with symmetrical and asymmetrical features were selected based on the Facial Flow (FF) concept from the database of the Smile Designer app regarding anatomical facial points. Two smile designs were created for each selected case: one using Artificial Intelligence (AI) and one created manually. An online survey assessed participants’ preferences for the different smile designs. The chi-square test “Pearson’s and Fisher’s exact test (P)” was used to analyze the survey data. A total of 628 people completed the study. Dentists preferred the manually-created smile design for the first three cases. For Case 4, dentists who used the Smile Designer program preferred the manually-created design (55.88%), while those who did not use the program preferred the AI-generated design (55.84%). There was a significant difference in esthetic perception between dentists and dental students (p = 0.001) and between dentists and laypeople (p = 0.001) for Case 1, only between dentists and dental students (p = 0.003) for Case 2, and only between dentists and laypeople (p = 0.001) for Case 3. Furthermore, we found that females (p = 0.007) and orthodontists (p = 0.025) had a higher preference for the AI-generated design in this case compared to males and other dental specialties for Case 3. While age, education level, and clinical experience did not significantly impact dentists’ preference for manually-created or AI-generated smile designs (p > 0.05), our results suggest that there were some differences in preference for Case 3. Overall, our findings suggest that the use of AI-generated smile designs for symmetric faces is acceptable to both dentists and laypeople and can offer time-saving benefits for clinicians. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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13 pages, 5162 KiB

Open AccessArticle

Root Dilaceration Using Deep Learning: A Diagnostic Approach

by Berrin Çelik and Mahmut Emin Çelik

Appl. Sci. 2023, 13(14), 8260; https://doi.org/10.3390/app13148260 - 17 Jul 2023

Cited by 1 | Viewed by 1975

Abstract

Understanding usual anatomical structures and unusual root formations is crucial for root canal treatment and surgical treatments. Root dilaceration is a tooth formation with sharp bends or curves, which causes dental treatments to fail, especially root canal treatments. The aim of the study was to apply recent deep learning models to develop an artificial intelligence-based computer-aided detection system for root dilaceration in panoramic radiographs. A total of 983 objects in 636 anonymized panoramic radiographs were initially labelled by an oral and maxillofacial radiologist and were then used to detect root dilacerations. A total of 19 state-of-the-art deep learning models with distinct backbones or feature extractors were used with the integration of alternative frameworks. Evaluation was carried out using Common Objects in Context (COCO) detection evaluation metrics, mean average precision (mAP), accuracy, precision, recall, F1 score and area under precision-recall curve (AUC). The duration of training was also noted for each model. Considering the detection performance of all models, mAP, accuracy, precision, recall, and F1 scores of up to 0.92, 0.72, 0.91, 0.87 and 0.83, respectively, were obtained. AUC were also analyzed to better understand where errors originated. It was seen that background confusion limited performance. The proposed system can facilitate root dilaceration assessment and alleviate the burden of clinicians, especially for endodontists and surgeons. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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10 pages, 3764 KiB

Open AccessArticle

Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach

by Jihye Ryu, Dong-Min Lee, Yun-Hoa Jung, OhJin Kwon, SunYoung Park, JaeJoon Hwang and Jae-Yeol Lee

Appl. Sci. 2023, 13(9), 5261; https://doi.org/10.3390/app13095261 - 23 Apr 2023

Cited by 4 | Viewed by 2383

Abstract

(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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12 pages, 2287 KiB

Open AccessArticle

Performance of Automated Oral Cancer Screening Algorithm in Tobacco Users vs. Non-Tobacco Users

by Susan Meishan Yang, Bofan Song, Cherie Wink, Mary Abouakl, Thair Takesh, Michelle Hurlbutt, Dana Dinica, Amber Davis, Rongguang Liang and Petra Wilder-Smith

Appl. Sci. 2023, 13(5), 3370; https://doi.org/10.3390/app13053370 - 06 Mar 2023

Cited by 2 | Viewed by 2096

Abstract

Oral non-neoplastic and neoplastic lesions have similar clinical manifestations, increasing the risk of inaccurate screening decisions that adversely affect oral cancer (OC) outcomes. Tobacco-use-related changes in the oral soft tissues may affect the accuracy of “smart” oral screening modalities. Because smoking is such a strong predictor of OC risk, it may overwhelm the impact of other variables on algorithm performance. The objective was to evaluate the screening accuracy in tobacco users vs. non-users of a previously developed prototype smartphone and machine-learning algorithm-based oral health screening modality. 318 subjects with healthy mucosa or oral lesions were allocated into either a “tobacco smoker” group or a “tobacco non-smoker” group. Next, intraoral autofluorescence (AFI) and polarized white light images (pWLI), risk factors as well as clinical signs and symptoms were recorded using the prototype screening platform. OC risk status as determined by the algorithm was compared with OC risk evaluation by an oral medicine specialist (gold standard). The screening platform achieved 80.0% sensitivity, 87.5% specificity, 83.67% agreement with specialist screening outcome in tobacco smokers, and 62.1% sensitivity, 82.9% specificity, 73.1% agreement with specialist screening outcome in non-smokers. Tobacco use should be carefully weighted as a variable in the architecture of any imaging-based screening algorithm for OC risk. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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19 pages, 5561 KiB

Open AccessArticle

Performance Analysis of Supervised Machine Learning Algorithms for Automatized Radiographical Classification of Maxillary Third Molar Impaction

by Andreas Vollmer, Michael Vollmer, Gernot Lang, Anton Straub, Alexander Kübler, Sebastian Gubik, Roman C. Brands, Stefan Hartmann and Babak Saravi

Appl. Sci. 2022, 12(13), 6740; https://doi.org/10.3390/app12136740 - 03 Jul 2022

Cited by 3 | Viewed by 3707

Abstract

Background: Oro-antral communication (OAC) is a common complication following the extraction of upper molar teeth. The Archer and the Root Sinus (RS) systems can be used to classify impacted teeth in panoramic radiographs. The Archer classes B-D and the Root Sinus classes III, IV have been associated with an increased risk of OAC following tooth extraction in the upper molar region. In our previous study, we found that panoramic radiographs are not reliable for predicting OAC. This study aimed to (1) determine the feasibility of automating the classification (Archer/RS classes) of impacted teeth from panoramic radiographs, (2) determine the distribution of OAC stratified by classification system classes for the purposes of decision tree construction, and (3) determine the feasibility of automating the prediction of OAC utilizing the mentioned classification systems. Methods: We utilized multiple supervised pre-trained machine learning models (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), one custom-made convolutional neural network (CNN) model, and a Bag of Visual Words (BoVW) technique to evaluate the performance to predict the clinical classification systems RS and Archer from panoramic radiographs (Aim 1). We then used Chi-square Automatic Interaction Detectors (CHAID) to determine the distribution of OAC stratified by the Archer/RS classes to introduce a decision tree for simple use in clinics (Aim 2). Lastly, we tested the ability of a multilayer perceptron artificial neural network (MLP) and a radial basis function neural network (RBNN) to predict OAC based on the high-risk classes RS III, IV, and Archer B-D (Aim 3). Results: We achieved accuracies of up to 0.771 for EfficientNet and MobileNetV2 when examining the Archer classification. For the AUC, we obtained values of up to 0.902 for our custom-made CNN. In comparison, the detection of the RS classification achieved accuracies of up to 0.792 for the BoVW and an AUC of up to 0.716 for our custom-made CNN. Overall, the Archer classification was detected more reliably than the RS classification when considering all algorithms. CHAID predicted 77.4% correctness for the Archer classification and 81.4% for the RS classification. MLP (AUC: 0.590) and RBNN (AUC: 0.590) for the Archer classification as well as MLP 0.638) and RBNN (0.630) for the RS classification did not show sufficient predictive capability for OAC. Conclusions: The results reveal that impacted teeth can be classified using panoramic radiographs (best AUC: 0.902), and the classification systems can be stratified according to their relationship to OAC (81.4% correct for RS classification). However, the Archer and RS classes did not achieve satisfactory AUCs for predicting OAC (best AUC: 0.638). Additional research is needed to validate the results externally and to develop a reliable risk stratification tool based on the present findings. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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12 pages, 872 KiB

Open AccessArticle

A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

by Élisson da Silva Rocha and Patricia Takako Endo

Appl. Sci. 2022, 12(6), 3103; https://doi.org/10.3390/app12063103 - 18 Mar 2022

Cited by 8 | Viewed by 2344

Abstract

Introduction: Dental segmentation in panoramic radiograph has become very relevant in dentistry, since it allows health professionals to carry out their assessments more clearly and helps them to define the best possible treatment plan for their patients. Objectives: In this work, a comparative study is carried out with four segmentation algorithms (U-Net, DCU-Net, DoubleU-Net and Nano-Net) that are prominent in the medical literature on segmentation and we evaluate their results with the current state of the art of dental segmentation in panoramic radiograph. Methods: These algorithms were tested with a dataset consisting of 1500 images, considering experiment scenarios with and without augmentation data. Results: DoubleU-Net was the model that presented the best results among the analyzed models, reaching 96.591% accuracy and 92.886% Dice using the dataset with data augmentation. Another model that stood out was Nano-Net using the dataset without data augmentation; this model achieved results close to that of the literature with only 235 thousand trainable parameters, while the literature model (TSASNet) contains 78 million. Conclusions: The results obtained in this work are satisfactory and present paths for a better and more effective dental segmentation process. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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10 pages, 265 KiB

Open AccessPerspective

Applications of Artificial Intelligence in Orthodontics—An Overview and Perspective Based on the Current State of the Art

by Felix Kunz, Angelika Stellzig-Eisenhauer and Julian Boldt

Appl. Sci. 2023, 13(6), 3850; https://doi.org/10.3390/app13063850 - 17 Mar 2023

Cited by 5 | Viewed by 6081

Abstract

Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care. Full article

(This article belongs to the Special Issue Artificial Intelligence Applied to Dentistry)

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