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
share announcement

Advancements in Artificial Intelligence for Dentomaxillofacial Radiology

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 December 2024 | Viewed by 6019

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
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 the diagnostic accuracy. AI algorithms have been developed to efficiently analyze and interpret complex medical and dental images, enabling 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 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 polices 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

Jump to: Other

14 pages, 4246 KiB  
Article
Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT
by Wojciech Kazimierczak, Róża Wajer, Oskar Komisarek, Marta Dyszkiewicz-Konwińska, Adrian Wajer, Natalia Kazimierczak, Joanna Janiszewska-Olszowska and Zbigniew Serafin
Diagnostics 2024, 14(21), 2410; https://doi.org/10.3390/diagnostics14212410 - 29 Oct 2024
Viewed by 222
Abstract
Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, [...] Read more.
Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)
►▼ Show Figures

Figure 1

15 pages, 3343 KiB  
Article
Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging
by Hakan Amasya, Mustafa Alkhader, Gözde Serindere, Karolina Futyma-Gąbka, Ceren Aktuna Belgin, Maxim Gusarev, Matvey Ezhov, Ingrid Różyło-Kalinowska, Merve Önder, Alex Sanders, Andre Luiz Ferreira Costa, Sérgio Lúcio Pereira de Castro Lopes and Kaan Orhan
Diagnostics 2023, 13(22), 3471; https://doi.org/10.3390/diagnostics13223471 - 18 Nov 2023
Cited by 4 | Viewed by 1908
Abstract
This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three [...] Read more.
This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as ‘presence of caries’ and 13,928 surfaces are determined as ‘absence of caries’ for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)
►▼ Show Figures

Figure 1

Other

Jump to: Research

21 pages, 1261 KiB  
Systematic Review
Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review
by Wael I. Ibraheem
Diagnostics 2024, 14(8), 806; https://doi.org/10.3390/diagnostics14080806 - 11 Apr 2024
Cited by 2 | Viewed by 2145
Abstract
Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success [...] Read more.
Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was “What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?” Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)
►▼ Show Figures

Figure 1

30 pages, 15168 KiB  
Case Report
The Stresses and Deformations in the Abfraction Lesions of the Lower Premolars Studied by the Finite Element Analyses: Case Report and Review of Literature
by Bogdan Constantin Costăchel, Anamaria Bechir, Mihail Târcolea, Lelia Laurența Mihai, Alexandru Burcea and Edwin Sever Bechir
Diagnostics 2024, 14(8), 788; https://doi.org/10.3390/diagnostics14080788 - 9 Apr 2024
Viewed by 1051
Abstract
Background: The purpose of the study was to investigate the behavior of hard dental structures of the teeth with abfraction lesions when experimental occlusal loads were applied. Methods: A 65-year-old patient came to the dentist because she had painful sensitivity in the temporomandibular [...] Read more.
Background: The purpose of the study was to investigate the behavior of hard dental structures of the teeth with abfraction lesions when experimental occlusal loads were applied. Methods: A 65-year-old patient came to the dentist because she had painful sensitivity in the temporomandibular joints and the lower right premolars. The patient was examined, and cone-beam computed tomography (CBCT) of the orofacial area was indicated. The data provided from the CBCT were processed with Mimics Innovation Suite 17 software to create the desired anatomical area in 3D format. Then, the structural calculation module was used in order to perform a finite element analysis of the lower right premolar teeth. A focused review of articles published between 2014 and 2023 from specialty literature regarding the FEA of premolars with abfraction lesions was also conducted. Results: The parcel area and the cervical third of the analyzed premolars proved to be the most vulnerable areas under the inclined direction of occlusal loads. The inclined application of experimental loads induced 3–4 times higher maximum shears, stresses, and deformations than the axial application of the same forces. Conclusions: FEA can be used to identify structural deficiencies in teeth with abfractions, a fact that is particularly important during dental treatments to correct occlusal imbalances. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Dentomaxillofacial Radiology)
►▼ 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