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Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives

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A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 28500

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Prof. Dr. Hang Nga Mai

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Guest Editor

Institute for Translational Research in Dentistry, Kyungpook National University, 2175 Dalgubeoldaero, Jung-gu, Daegu 41940, Republic of Korea
Interests: aesthetic dentistry; applied artificial intelligence; applied 3D image technology; biomaterials

Prof. Dr. Tim Joda

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Guest Editor

Head of eHealth in Reconstructive Dentistry, Clinic of Reconstructive Dentistry, Center for Dental Medicine, University of Zurich, 8006 Zürich, Switzerland
Interests: reconstructive dentistry; prosthodontics; implant dentistry; digital technology; dental materials; augmented/virtual reality; artificial intelligence; big data & eHealth; public health; translational research
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Dear Colleagues,

This Special Issue aims to explore the transformative role of artificial intelligence (AI) in the field of dentistry, shedding light on innovative applications, emerging technologies, and their potential impact on patient care. As the intersection of AI and dentistry continues to evolve, this Special Issue will feature cutting-edge research, reviews, and case studies that showcase the integration of AI algorithms, machine learning, and data analytics in various aspects of dental practice.

Revolutionizing traditional paradigms, this Special Issue will showcase how AI is reshaping diagnostic imaging, treatment planning, and patient management. Furthermore, it will unveil the transformative potential of machine learning and data analytics in streamlining dental practices, enhancing precision and optimizing treatment outcomes. From leveraging advanced algorithms for accurate diagnostics to incorporating state-of-the-art technologies for treatment personalization, the contributions published in this Special Issue will illuminate the multifaceted impact of AI on the entire spectrum of oral healthcare.

The aim of this Special Issue is not only to explore technological advancements, but also it is an invitation to envision a future where AI is not merely a tool but a transformative force in the pursuit of optimal oral health. Additionally, it will address challenges, ethical considerations, and opportunities for collaboration between dental professionals and AI experts.

Prof. Dr. Hang Nga Mai
Prof. Dr. Tim Joda
Guest Editors

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Keywords

artificial intelligence
big data in dentistry
explainable AI in dental diagnostics
blockchain in dental data security
natural language processing in dental records
robotics in dentistry
smart dental devices
AI in clinical decision support
automation in dentistry
AI-based diagnostic imaging
AI-supported treatment planning

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Published Papers (9 papers)

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Research

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17 pages, 2771 KiB

Open AccessArticle

An Artificial Intelligence-Based Fuzzy Logic System for Periodontitis Risk Assessment in Patients with Type 2 Diabetes Mellitus

by Ioana Scrobota, Gilda Mihaela Iova, Olivia Andreea Marcu, Liliana Sachelarie, Siviu Vlad, Ioana Monica Duncea and Florin Blaga

Bioengineering 2025, 12(3), 211; https://doi.org/10.3390/bioengineering12030211 - 20 Feb 2025

Viewed by 801

Abstract

Background: Since periodontitis prevalence has increased globally and there is a bidirectional relationship between periodontitis and diabetes mellitus (DM), new methods of preventing and screening involving DM biomarkers could impact periodontitis management. We aimed to develop a fuzzy system to estimate the risk of periodontitis in patients with DM. Methods: Body mass index (BMI), glycemia (G), total cholesterol (C), and triglyceride (T) measurements were collected from 87 patients diagnosed with DM. Oral examinations were performed, and the number of the periodontal pockets (nrPPs) was determined. A fuzzy system was developed: BMI and G as inputs resulted in Periodontitis Risk 1 (PRisk1) output; C and T as inputs resulted in Periodontitis Risk 2 (PRisk2) output. From PRisk1 and PRisk2, the cumulative periodontitis risk (PCRisk) was assessed. Linguistic terms and linguistic grades (very small, small, medium, big, and very big) were assigned to the numerical variables by using 25 different membership functions. PCRisk and nrPP values were statistically processed. Results: In our developed fuzzy system, BMI, G, C, and T as input data resulted in periodontitis risk estimation. PCRisk was correlated with nrPP: when PCRisk increased by 1.881 units, nrPP increased by 1 unit. The fuzzy logic-based system effectively estimated periodontitis risk in type 2 diabetes patients, showing a significant correlation with the number of periodontal pockets. These findings highlight its potential for early diagnosis and improved interdisciplinary care. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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21 pages, 9329 KiB

Open AccessArticle

Automated Measurements of Tooth Size and Arch Widths on Cone-Beam Computerized Tomography and Scan Images of Plaster Dental Models

by Thong Phi Nguyen, Jang-Hoon Ahn, Hyun-Kyo Lim, Ami Kim and Jonghun Yoon

Bioengineering 2025, 12(1), 22; https://doi.org/10.3390/bioengineering12010022 - 29 Dec 2024

Viewed by 1455

Abstract

Measurements of tooth size for estimating inter-arch tooth size discrepancies and inter-tooth distances, essential for orthodontic diagnosis and treatment, are primarily done using traditional methods involving plaster models and calipers. These methods are time-consuming and labor-intensive, requiring multiple steps. With advances in cone-beam computerized tomography (CBCT) and intraoral scanning technology, these processes can now be automated through computer analyses. This study proposes a multi-step computational method for measuring mesiodistal tooth widths and inter-tooth distances, applicable to both CBCT and scan images of plaster models. The first step involves 3D segmentation of the upper and lower teeth using CBCT, combining results from sagittal and panoramic views. For intraoral scans, teeth are segmented from the gums. The second step identifies the teeth based on an adaptively estimated jaw midline using maximum intensity projection. The third step uses a decentralized convolutional neural network to calculate key points representing the parameters. The proposed method was validated against manual measurements by orthodontists using plaster models, achieving an intraclass correlation coefficient of 0.967 and a mean absolute error of less than 1 mm for all tooth types. An analysis of variance test confirmed the statistical consistency between the method’s measurements and those of human experts. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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15 pages, 5686 KiB

Open AccessArticle

Integrating Super-Resolution with Deep Learning for Enhanced Periodontal Bone Loss Segmentation in Panoramic Radiographs

by Vungsovanreach Kong, Eun Young Lee, Kyung Ah Kim and Ho Sun Shon

Bioengineering 2024, 11(11), 1130; https://doi.org/10.3390/bioengineering11111130 - 8 Nov 2024

Cited by 1 | Viewed by 1103

Abstract

Periodontal disease is a widespread global health concern that necessitates an accurate diagnosis for effective treatment. Traditional diagnostic methods based on panoramic radiographs are often limited by subjective evaluation and low-resolution imaging, leading to suboptimal precision. This study presents an approach that integrates Super-Resolution Generative Adversarial Networks (SRGANs) with deep learning-based segmentation models to enhance the segmentation of periodontal bone loss (PBL) areas on panoramic radiographs. By transforming low-resolution images into high-resolution versions, the proposed method reveals critical anatomical details that are essential for precise diagnostics. The effectiveness of this approach was validated using datasets from the Chungbuk National University Hospital and the Kaggle data portal, demonstrating significant improvements in both image resolution and segmentation accuracy. The SRGAN model, evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, achieved a PSNR of 30.10 dB and an SSIM of 0.878, indicating high fidelity in image reconstruction. When applied to semantic segmentation using a U-Net architecture, the enhanced images resulted in a dice similarity coefficient (DSC) of 0.91 and an intersection over union (IoU) of 84.9%, compared with 0.72 DSC and 65.4% IoU for native low-resolution images. These results underscore the potential of SRGAN-enhanced imaging to improve PBL area segmentation and suggest broader applications in medical imaging, where enhanced image clarity is crucial for diagnostic accuracy. This study also highlights the importance of further research to expand the dataset diversity and incorporate clinical validation to fully realize the benefits of super-resolution techniques in medical diagnostics. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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12 pages, 1046 KiB

Open AccessArticle

Teledentistry for Pediatric Dental Emergency: Comparison Between Experienced and Novice Users

by Chih-Chieh Huang and Jung-Wei Chen

Bioengineering 2024, 11(11), 1054; https://doi.org/10.3390/bioengineering11111054 - 23 Oct 2024

Viewed by 1121

Abstract

Background: During the COVID-19 pandemic, teledentistry was often employed for pediatric emergency treatments. Dental students acted as the first health providers using teledentistry under the supervision of faculties in most hospital-based or university-based medical centers during the lockdown period. The aims of this study were to assess the quality of using teledentistry among general dentists (GDs) and dental students (DSs) for managing pediatric dental emergencies. Methods: In total, 60 DSs and 85 GDs were recruited in this study. Each participant was assigned to one of five teledentistry emergency scenarios in pediatric dentistry using a stratified random assignment method. Teledentistry with five emergency scenario simulations was used to evaluate the quality of diagnosis (QD) and treatment (QT) and the detailed information (DI) among all participants. A post-visit survey collected demographic data, usability, confidence in diagnosis (CD), and confidence in treatment recommendation (CT). Descriptive and inferential statistics data were analyzed. The significance level was set as p < 0.05. Results: Overall, the study showed that GDs and DSs can use teledentistry to provide good quality of diagnosis (74.5%) and treatment recommendations (77.2%). When encountering pediatric dental emergency scenarios, GDs scored significantly higher (p < 0.001) than DSs regarding QD, QT, CD, and CT. Significant differences were noted in QD (p < 0.001), QT (p < 0.001), CD (p = 0.045), and DI (p = 0.042) when the subjects encountered five different scenarios. Significant correlations were noted between the amount of detailed information subjects obtained with the quality of diagnosis and treatment recommendation. Confidence in diagnosis is significantly correlated to the quality of diagnosis (p = 0.034) and treatment recommendation (p = 0.042). However, the confidence in treatment recommendation is not correlated with either QD or QT. Both GDs and DSs hold positive attitudes toward the usability of teledentistry. Conclusions: Teledentistry is effective for diagnosing and managing pediatric dental emergencies. Experienced users provided a better quality of visit compared to novice users, so dental students should be supervised when performing a teledentistry visit. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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14 pages, 4289 KiB

Open AccessArticle

Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs

by Rohan Jagtap, Yalamanchili Samata, Amisha Parekh, Pedro Tretto, Michael D. Roach, Saranu Sethumanjusha, Chennupati Tejaswi, Prashant Jaju, Alan Friedel, Michelle Briner Garrido, Maxine Feinberg and Mini Suri

Bioengineering 2024, 11(10), 1001; https://doi.org/10.3390/bioengineering11101001 - 5 Oct 2024

Viewed by 1582

Abstract

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7–0.73), implants (0.97–0.98), restored teeth (0.85–0.89), teeth with fixed prosthesis (0.92–0.94), and missing teeth (0.82–0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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10 pages, 1611 KiB

Open AccessArticle

Effectiveness of Machine Learning in Predicting Orthodontic Tooth Extractions: A Multi-Institutional Study

by Lily E. Etemad, J. Parker Heiner, A. A. Amin, Tai-Hsien Wu, Wei-Lun Chao, Shin-Jung Hsieh, Zongyang Sun, Camille Guez and Ching-Chang Ko

Bioengineering 2024, 11(9), 888; https://doi.org/10.3390/bioengineering11090888 - 31 Aug 2024

Cited by 2 | Viewed by 1792

Abstract

The study aimed to evaluate the effectiveness of machine learning in predicting whether orthodontic patients would require extraction or non-extraction treatment using data from two university datasets. A total of 1135 patients, with 297 from University 1 and 838 from University 2, were included during consecutive enrollment periods. The study identified 20 inputs including 9 clinical features and 11 cephalometric measurements based on previous research. Random forest (RF) models were used to make predictions for both institutions. The performance of each model was assessed using sensitivity (SEN), specificity (SPE), accuracy (ACC), and feature ranking. The model trained on the combined data from two universities demonstrated the highest performance, achieving 50% sensitivity, 97% specificity, and 85% accuracy. When cross-predicting, where the University 1 (U1) model was applied to the University 2 (U2) data and vice versa, there was a slight decrease in performance metrics (ranging from 0% to 20%). Maxillary and mandibular crowding were identified as the most significant features influencing extraction decisions in both institutions. This study is among the first to utilize datasets from two United States institutions, marking progress toward developing an artificial intelligence model to support orthodontists in clinical practice. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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19 pages, 7119 KiB

Open AccessArticle

Digital Dental Biometrics for Human Identification Based on Automated 3D Point Cloud Feature Extraction and Registration

by Yu Zhou, Li Yuan, Yanfeng Li and Jiannan Yu

Bioengineering 2024, 11(9), 873; https://doi.org/10.3390/bioengineering11090873 - 28 Aug 2024

Cited by 1 | Viewed by 1420

Abstract

Background: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. Methods: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. Results: Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. Conclusions: The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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13 pages, 3258 KiB

Open AccessArticle

Artificial Intelligence for Predicting the Aesthetic Component of the Index of Orthodontic Treatment Need

by Leah Stetzel, Florence Foucher, Seung Jin Jang, Tai-Hsien Wu, Henry Fields, Fernanda Schumacher, Stephen Richmond and Ching-Chang Ko

Bioengineering 2024, 11(9), 861; https://doi.org/10.3390/bioengineering11090861 - 23 Aug 2024

Viewed by 13751

Abstract

The aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN) is internationally recognized as a reliable and valid method for assessing aesthetic treatment need. The objective of this study is to use artificial intelligence (AI) to automate the AC assessment. A total of 1009 pre-treatment frontal intraoral photos with overjet values were collected. Each photo was graded by an experienced calibration clinician. The AI was trained using the intraoral images, overjet, and two other approaches. For Scheme 1, the training data were AC 1–10. For Scheme 2, the training data were either the two groups AC 1–5 and AC 6–10 or the three groups AC 1–4, AC 5–7, and AC 8–10. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were measured for all approaches. The performance was tested without overjet values as input. The intra-rater reliability for the grader, using kappa, was 0.84 (95% CI 0.76–0.93). Scheme 1 had 77% sensitivity, 88% specificity, 82% accuracy, 89% PPV, and 75% NPV in predicting the binary groups. All other schemes offered poor tradeoffs. Findings after omitting overjet and dataset supplementation results were mixed, depending upon perspective. We have developed deep learning-based algorithms that can predict treatment need based on IOTN-AC reference standards; this provides an adjunct to clinical assessment of dental aesthetics. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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Review

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24 pages, 3648 KiB

Open AccessReview

Artificial Intelligence in Dentistry: A Descriptive Review

by Sreekanth Kumar Mallineni, Mallika Sethi, Dedeepya Punugoti, Sunil Babu Kotha, Zikra Alkhayal, Sarah Mubaraki, Fatmah Nasser Almotawah, Sree Lalita Kotha, Rishitha Sajja, Venkatesh Nettam, Amar Ashok Thakare and Srinivasulu Sakhamuri

Bioengineering 2024, 11(12), 1267; https://doi.org/10.3390/bioengineering11121267 - 13 Dec 2024

Cited by 3 | Viewed by 4369

Abstract

Artificial intelligence (AI) is an area of computer science that focuses on designing machines or systems that can perform operations that would typically need human intelligence. AI is a rapidly developing technology that has grabbed the interest of researchers from all across the globe in the healthcare industry. Advancements in machine learning and data analysis have revolutionized oral health diagnosis, treatment, and management, making it a transformative force in healthcare, particularly in dentistry. Particularly in dentistry, AI is becoming increasingly prevalent as it contributes to the diagnosis of oro-facial diseases, offers treatment modalities, and manages practice in the dental operatory. All dental disciplines, including oral medicine, operative dentistry, pediatric dentistry, periodontology, orthodontics, oral and maxillofacial surgery, prosthodontics, and forensic odontology, have adopted AI. The majority of AI applications in dentistry are for diagnoses based on radiographic or optical images, while other tasks are less applicable due to constraints such as data availability, uniformity, and computational power. Evidence-based dentistry is considered the gold standard for decision making by dental professionals, while AI machine learning models learn from human expertise. Dentistry AI and technology systems can provide numerous benefits, such as improved diagnosis accuracy and increased administrative task efficiency. Dental practices are already implementing various AI applications, such as imaging and diagnosis, treatment planning, robotics and automation, augmented and virtual reality, data analysis and predictive analytics, and administrative support. The dentistry field has extensively used artificial intelligence to assist less-skilled practitioners in reaching a more precise diagnosis. These AI models effectively recognize and classify patients with various oro-facial problems into different risk categories, both individually and on a group basis. The objective of this descriptive review is to review the most recent developments of AI in the field of dentistry. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dentistry: Innovations, Applications, and Future Perspectives)

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