Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
Diagnostics
Special Issues
Advancements in Artificial Intelligence for Dentomaxillofacial Radiology—2nd Edition
share announcement

Advancements in Artificial Intelligence for Dentomaxillofacial Radiology—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: 31 October 2025 | Viewed by 3390

Special Issue Editors

Prof. Dr. Andre Luiz Ferreira Costa

E-Mail Website
Guest Editor
Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo, SP, Brazil
Interests: dentistry; dentomaxillofacial imaging; MRI/CBCT/CT; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kaan Orhan

E-Mail Website
Guest Editor
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
Interests: radiology; MRI/CBCT/CT/USG; dentistry; head and neck imaging; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sérgio Lúcio Pereira de Castro Lopes

E-Mail Website
Guest Editor
Department of Diagnosis and Surgery, São José dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos 2245-000, SP, Brazil
Interests: dentomaxillofacial radiology; MRI; CBCT; computer-assisted diagnosis; TMJ
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has significantly transformed the field of dentomaxillofacial radiology, improving diagnostic accuracy. AI algorithms have been developed to efficiently analyze and interpret complex medical and dental images, enabling the faster and more precise detection of dental and maxillofacial pathologies. By leveraging deep learning and machine learning techniques, AI systems can detect abnormalities such as caries, fractures, and tumors with higher sensitivity and specificity, in addition to hidden aspects such as aspects of shape, intensity, and texture in the images. Therefore, this Special Issue is focused on the latest developments of AI in dentomaxillofacial radiology, encompassing the insights of computer-aided diagnosis, radiomics, and machine learning tools for image interpretation.

Prof. Dr. Andre Luiz Ferreira Costa
Prof. Dr. Kaan Orhan
Prof. Dr. Sérgio Lúcio Pereira de Castro Lopes
Guest Editors

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
  • deep learning
  • dentomaxillofacial radiology
  • dentistry
  • diagnosis

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 1178 KiB  
Article
Automatic Detection of Radiographic Alveolar Bone Loss in Bitewing and Periapical Intraoral Radiographs Using Deep Learning Technology: A Preliminary Evaluation
by Amjad AlGhaihab, Antonio J. Moretti, Jonathan Reside, Lyudmila Tuzova, Yiing-Shiuan Huang and Donald A. Tyndall
Diagnostics 2025, 15(5), 576; https://doi.org/10.3390/diagnostics15050576 - 27 Feb 2025
Viewed by 716
Abstract
Background/Objective: Periodontal disease is a prevalent inflammatory condition affecting the supporting structures of teeth, with radiographic bone loss (RBL) being a critical diagnostic marker. The accurate and consistent evaluation of RBL is essential for the staging and grading of periodontitis, as outlined by [...] Read more.
Background/Objective: Periodontal disease is a prevalent inflammatory condition affecting the supporting structures of teeth, with radiographic bone loss (RBL) being a critical diagnostic marker. The accurate and consistent evaluation of RBL is essential for the staging and grading of periodontitis, as outlined by the 2017 AAP/EFP Classification. Advanced tools such as deep learning (DL) technology, including Denti.AI, an FDA-cleared software utilizing convolutional neural networks (CNNs), offer the potential for enhancing diagnostic accuracy. This study evaluated the diagnostic accuracy of Denti.AI for detecting RBL in intraoral radiographs. Methods: A dataset of 39 intraoral radiographs (22 periapical and 17 bitewing), covering 316 tooth surfaces (123 periapical and 193 bitewing), was selected from a de-identified pool of 500 radiographs provided by Denti.AI. RBL was assessed using the 2017 AAP/EFP Classification. A consensus panel of three board-certified dental specialists served as the reference standard. Performance metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and mean absolute error (MAE), were calculated. Results: For periapical radiographs, Denti.AI achieved a sensitivity of 76%, specificity of 86%, PPV of 83%, NPV of 80%, and accuracy of 81%, with an MAE of 0.046%. For bitewing radiographs, sensitivity was 65%, specificity was 90%, PPV was 88%, NPV was 70%, and accuracy was 76%, with an MAE of 0.499 mm. Conclusions: Denti.AI demonstrated clinically acceptable performance in detecting RBL and shows potential as an adjunctive diagnostic tool, supporting clinical decision-making. While performance was robust for periapical radiographs, further optimization may enhance its accuracy for bitewing radiographs. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology—2nd Edition)
►▼ Show Figures

Figure 1

13 pages, 1364 KiB  
Article
Artificial Intelligence-Supported and App-Aided Cephalometric Analysis: Which One Can We Trust?
by Senol Koz and Ozge Uslu-Akcam
Diagnostics 2025, 15(5), 559; https://doi.org/10.3390/diagnostics15050559 - 26 Feb 2025
Viewed by 605
Abstract
Background: This study aimed to compare the reproducibility and reliability of the AI-supported WebCeph and app-aided OneCeph cephalometric analysis programs with a manual analysis method and to evaluate the analysis times. Methods: The study material consisted of pretreatment lateral cephalograms from [...] Read more.
Background: This study aimed to compare the reproducibility and reliability of the AI-supported WebCeph and app-aided OneCeph cephalometric analysis programs with a manual analysis method and to evaluate the analysis times. Methods: The study material consisted of pretreatment lateral cephalograms from 110 cases. Cephalometric analyses were performed manually, using the WebCeph program, and using the OneCeph application. A total of 11 skeletal, 6 dental, and 3 soft tissue parameters were measured. Cephalometric analyses of 30 randomly selected cases were performed again using three methods. The analysis times were recorded. Results: The WebCeph program and OneCeph application are highly compatible with the manual analysis method in terms of all parameters, except for SN measurement. It was found that the WebCeph program and the OneCeph application demonstrated moderate agreement in U1-NA distance measurement, while statistically high agreement was observed among all three methods for other dental parameters. It was determined that there was a moderate agreement among the methods in terms of nasolabial angle, whereas a statistically high level of agreement was found for the other soft tissue parameters. The analysis time was found to be the lowest in the WebCeph program and the highest in the manual analysis method. Conclusions: The WebCeph program and OneCeph application showed a high degree of compatibility with the manual analysis method, except for SN, SNA, Gonial angle, Articular angle, U1-NA distance and nasolabial angle measurements. Due to the higher correlation between OneCeph and the manual method, it can be concluded that the OneCeph application is the best alternative to the manual method. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology—2nd Edition)
►▼ Show Figures

Figure 1

12 pages, 1203 KiB  
Article
Deep Learning-Based Periapical Lesion Detection on Panoramic Radiographs
by Viktor Szabó, Kaan Orhan, Csaba Dobó-Nagy, Dániel Sándor Veres, David Manulis, Matvey Ezhov, Alex Sanders and Bence Tamás Szabó
Diagnostics 2025, 15(4), 510; https://doi.org/10.3390/diagnostics15040510 - 19 Feb 2025
Viewed by 1229
Abstract
Background/Objectives: Our study aimed to determine the accuracy of the artificial intelligence-based Diagnocat system (DC) in detecting periapical lesions (PL) on panoramic radiographs (PRs). Methods: 616 teeth were selected from 357 panoramic radiographs, including 308 teeth with clearly visible periapical radiolucency and [...] Read more.
Background/Objectives: Our study aimed to determine the accuracy of the artificial intelligence-based Diagnocat system (DC) in detecting periapical lesions (PL) on panoramic radiographs (PRs). Methods: 616 teeth were selected from 357 panoramic radiographs, including 308 teeth with clearly visible periapical radiolucency and 308 without any periapical lesion. Three groups were generated: teeth with radiographic signs of caries (Group 1), teeth with coronal restoration (Group 2), and teeth with root canal filling (Group 3). The PRs were uploaded to the Diagnocat system for evaluation. The performance of the convolutional neural network in detecting PLs was assessed by its sensitivity, specificity, and positive and negative predictive values, as well as the diagnostic accuracy value. We investigated the possible effect of the palatoglossal air space (PGAS) on the evaluation of the AI tool. Results: DC identified periapical lesions in 240 (77.9%) cases out of the 308 teeth with PL and detected no PL in 68 (22.1%) teeth with PL. The AI-based system detected no PL in any of the groups without PL. The overall sensitivity, specificity, and diagnostic accuracy of DC were 0.78, 1.00, and 0.89, respectively. Considering these parameters for each group, Group 2 showed the highest values at 0.84, 1.00, and 0.95, respectively. Fisher’s Exact test showed that PGAS does not significantly affect (p = 1) the detection of PL in the upper teeth. The AI-based system showed lower probability values for detecting PL in the case of central incisors, wisdom teeth, and canines. The sensitivity and diagnostic accuracy of DC for detecting PL on canines showed lower values at 0.27 and 0.64, respectively. Conclusions: The CNN-based Diagnocat system can support the diagnosis of PL on PRs and serves as a decision-support tool during radiographic assessments. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology—2nd Edition)
►▼ Show Figures

Figure 1

11 pages, 2335 KiB  
Article
An Assessment of Deep Learning’s Impact on General Dentists’ Ability to Detect Alveolar Bone Loss in 2D Intraoral Radiographs
by Amjad AlGhaihab, Antonio J. Moretti, Jonathan Reside, Lyudmila Tuzova and Donald A. Tyndall
Diagnostics 2025, 15(4), 467; https://doi.org/10.3390/diagnostics15040467 - 14 Feb 2025
Cited by 1 | Viewed by 550
Abstract
Background/Objective: Deep learning (DL) technology has shown potential in enhancing diagnostic accuracy in dentomaxillofacial radiology, particularly for detecting carious lesions, apical lesions, and periodontal bone loss. However, its effect on general dentists’ ability to detect radiographic bone loss (RBL) in clinical practice remains [...] Read more.
Background/Objective: Deep learning (DL) technology has shown potential in enhancing diagnostic accuracy in dentomaxillofacial radiology, particularly for detecting carious lesions, apical lesions, and periodontal bone loss. However, its effect on general dentists’ ability to detect radiographic bone loss (RBL) in clinical practice remains unclear. This study investigates the impact of the Denti.AI DL technology on general dentists’ ability to identify bone loss in intraoral radiographs, addressing this gap in the literature. Methods: Ten dentists from the university’s dental clinics independently assessed 26 intraoral radiographs (periapical and bitewing) for bone loss using a Likert scale probability index with and without DL assistance. The participants viewed images on identical monitors with controlled lighting. This study generated 3940 data points for analysis. The statistical analyses included receiver operating characteristic (ROC) curves, area under the curve (AUC), and ANOVA tests. Results: Most dentists showed minor improvement in detecting bone loss on periapical radiographs when using DL. For bitewing radiographs, only a few dentists showed minor improvement. Overall, the difference in diagnostic accuracy between evaluations with and without DL was minimal (0.008). The differences in AUC for periapical and bitewing radiographs were 0.031 and −0.009, respectively, and were not statistically significant. Conclusions: This study found no statistically significant improvement in experienced dentists’ diagnostic accuracy for detecting bone loss in intraoral radiographs when using Denti.AI deep learning technology. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology—2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop