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Artificial Intelligence in the Diagnostics of Dental Diseases, 2nd Edition
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Artificial Intelligence in the Diagnostics of Dental Diseases, 2nd Edition

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: 30 September 2025 | Viewed by 3161

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,

After a successful first edition of the Special Issue “Artificial Intelligence in the Diagnostics of Dental Disease” (https://www.mdpi.com/journal/diagnostics/special_issues/A727S78Y7L), we are pleased to announce a second edition.

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 analyze 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 analyze 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 (3 papers)

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Research

19 pages, 5166 KiB  
Article
Federated Learning-Based CNN Models for Orthodontic Skeletal Classification and Diagnosis
by Demet Süer Tümen and Mehmet Nergiz
Diagnostics 2025, 15(7), 920; https://doi.org/10.3390/diagnostics15070920 - 2 Apr 2025
Viewed by 513
Abstract
Background/Objectives: Accurate skeletal classification is essential for orthodontic diagnosis. This study evaluates the effectiveness of federated convolutional neural network (CNN) models for skeletal classification using cephalometric images from the ISBI and Dicle datasets. This research aims to evaluate the effectiveness of federated learning [...] Read more.
Background/Objectives: Accurate skeletal classification is essential for orthodontic diagnosis. This study evaluates the effectiveness of federated convolutional neural network (CNN) models for skeletal classification using cephalometric images from the ISBI and Dicle datasets. This research aims to evaluate the effectiveness of federated learning (FL) for orthodontic skeletal classification by comparing its performance against centralized learning (CL) and local learning (LL). The objective is to determine whether FL can achieve competitive performance while preserving data privacy and enabling collaborative model training across multiple institutions. Methods: The DenseNet121 model and its augmented versions, incorporating channel attention, spatial attention, squeeze and excitation, and spatial pyramid pooling blocks, are proposed and adapted for the study. Models are evaluated on the ISBI and Dicle datasets using accuracy, sensitivity, and specificity metrics, with performance gains benchmarked across CL, LL, and FL frameworks. Results: Accuracy improvements exceed 26% compared to the baseline model on FL framework. The DenseNet121_SA model, augmented with spatial pyramid pooling blocks, achieves a 20.86% performance gain over LL settings on the ISBI dataset. Similarly, the DenseNet121_SA model, augmented with spatial attention, and DenseNet121_SA_SE model, augmented with spatial attention and squeeze and excitation, obtain 16.58% and 15.22% by not sacrificing performance loss with respect to CL. The inclusion of the Dicle dataset provides additional validation for the models. Conclusions: Federated CNN models exhibit significant promise for orthodontic skeletal classification. These models demonstrate the potential of FL to enhance collaborative model training while preserving data privacy. This approach represents a step forward in leveraging precise orthodontic diagnostics technology by enabling a data-secure collaborative artificial intelligence among various orthodontic clinics. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Diseases, 2nd Edition)
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16 pages, 2280 KiB  
Article
Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study
by Ricardo Jorge Pais, João Botelho, Vanessa Machado, Gil Alcoforado, José João Mendes, Ricardo Alves and Lucinda J. Bessa
Diagnostics 2025, 15(4), 425; https://doi.org/10.3390/diagnostics15040425 - 10 Feb 2025
Cited by 1 | Viewed by 926
Abstract
Background: Machine learning (ML) techniques have been recently proposed as a solution for aiding in the prevention and diagnosis of microbiome-related diseases. Here, we applied auto-ML approaches on real-case metagenomic datasets from saliva and subgingival peri-implant biofilm microbiomes to explore a wide range [...] Read more.
Background: Machine learning (ML) techniques have been recently proposed as a solution for aiding in the prevention and diagnosis of microbiome-related diseases. Here, we applied auto-ML approaches on real-case metagenomic datasets from saliva and subgingival peri-implant biofilm microbiomes to explore a wide range of ML algorithms to benchmark best-performing algorithms for predicting peri-implantitis (PI). Methods: A total of 100 metagenomes from the NCBI SRA database (PRJNA1163384) were used in this study to construct biofilm and saliva metagenomes datasets. Two AI-driven auto-ML approaches were used on constructed datasets to generate 100 ML-based models for the prediction of PI. These were compared with statistically significant single-microorganism-based models. Results: Several ML algorithms were pinpointed as suitable bespoke predictive approaches to apply to metagenomic data, outperforming the single-microorganism-based classification. Auto-ML approaches rendered high-performing models with Receiver Operating Characteristic–Area Under the Curve, sensitivities and specificities between 80% and 100%. Among these, classifiers based on ML-driven scoring of combinations of 2–4 microorganisms presented top-ranked performances and can be suitable for clinical application. Moreover, models generated based on the saliva microbiome showed higher predictive performance than those from the biofilm microbiome. Conclusions: This feasibility study bridges complex AI research with practical dental applications by benchmarking ML algorithms and exploring oral microbiomes as foundations for developing intuitive, cost-effective, and clinically relevant diagnostic platforms. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Diseases, 2nd Edition)
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21 pages, 8297 KiB  
Article
Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs
by Deniz Bora Küçük, Andaç Imak, Salih Taha Alperen Özçelik, Adalet Çelebi, Muammer Türkoğlu, Abdulkadir Sengur and Deepika Koundal
Diagnostics 2025, 15(3), 244; https://doi.org/10.3390/diagnostics15030244 - 22 Jan 2025
Cited by 1 | Viewed by 1359
Abstract
Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate [...] Read more.
Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model’s performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model’s balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model’s generalizability. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Diagnostics of Dental Diseases, 2nd Edition)
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