Applications of Dentomaxillofacial Diagnostic Imaging in Different Specialties

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2440

Special Issue Editor


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Guest Editor
Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
Interests: dentomaxillofacial radiology; CBCT; digital radiology; implant radiology; micro CT; T rays and dentistry
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Special Issue Information

Dear Colleagues,

Dentomaxillofacial Radiology (DMFR) is one of the dental specialties recognized under different names and divisions all over the world. Diagnostic imaging techniques, specifically X ray imaging, have always been a tremendous asset in clinical dentistry. The use of various imaging techniques is essential in the field of restorative and endodontic treatment, periodontal assessment, prosthetic rehabilitation and surgical procedures including implant placement and orthodontics for different tasks. However, scientists have been searching for safer and comparable alternative imaging modalities to X-ray imaging due to increasing concerns regarding the radiation dose and economic limitations. Today, the imaging procedures utilized to assess oral and maxillofacial regions comprise intraoral imaging, dental panoramic imaging, cephalometric imaging, sialography, cone beam computed tomography (CBCT), multislice medical computed tomography, ultrasonography (US), magnetic resonance imaging (MRI), Positron Emission Tomography (PET) and Scintigraphy. In addition, visible light, optical coherence tomography, and terahertz imaging are other methods in use or under investigation. In recent years, 3D printing technology has become popular, which is a process whereby a given material is deposited in successive layers to create a 3D object. In dentistry, this technology involves three steps: digital data acquisition using a scanner and/or CBCT, data processing and design within a software application, and manufacturing through 3D printing. Furthermore, 3D-printing technology may be utilized for education and research purposes, and may be helpful to reduce the surgical time, operator bias, and the risk of procedural errors. In addition, applications of computer-aided and image-guided procedures with Haptic and Robotic devices are in progress. Recently, there has been much interest in the development of Artificial Intelligence (AI) applications. Dentomaxillofacial Radiology is within the scope of these applications due to its compatibility with image processing methods. Further research in the field of AI will make great contributions to dental diagnostic imaging. It is expected that AI will help to reduce the daily workload of physicians as well as the rate of false diagnosis in dental practice. It should be kept in mind that diagnostic images obtained from the dentomaxillofacial region may also show part or the entire nasal cavity, paranasal sinuses, airway, cervical vertebrae, and temporal bone. Incidental findings may require follow-up, and further treatment options may be identified in conjunction with clinical findings, including referral to a specialist not directly linked to the field of dentistry in certain cases. It is obvious that further research in the field of DMFR has the potential to make great contributions to dental and medical clinical practice. This Special Issue is designed for anyone who consults, performs, interprets, or uses dentomaxillofacial imaging procedures, including clinicians, specialists, ENT specialists, head and neck radiologists, and oral and maxillofacial radiologists.

Prof. Dr. Kıvanç Kamburoğlu
Guest Editor

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Keywords

  • radiology
  • DMFR
  • diagnostic imaging
  • dentistry

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Published Papers (3 papers)

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Research

11 pages, 3929 KiB  
Article
The Accuracy of Intraoral Scanners in Maxillary Defects with Different Model Variations
by Sema Murat, Burcu Batak, Özge Aydoğ and Caner Öztürk
Diagnostics 2024, 14(21), 2368; https://doi.org/10.3390/diagnostics14212368 - 24 Oct 2024
Viewed by 417
Abstract
Background: Advances in digital technology and intraoral scanners (IOSs) have the potential to enable accurate digital impressions for patients with maxillary defects. This study aimed to compare the accuracy of IOSs in completely and partially edentulous models with maxillary defects. Methods: Three polyurethane [...] Read more.
Background: Advances in digital technology and intraoral scanners (IOSs) have the potential to enable accurate digital impressions for patients with maxillary defects. This study aimed to compare the accuracy of IOSs in completely and partially edentulous models with maxillary defects. Methods: Three polyurethane models—one completely edentulous (CE) and two partially edentulous, following Aramany classifications I (ACI) and II (ACII)—were created using stereolithography. These models were scanned with a desktop scanner to create reference models. Ten scans were performed using three different intraoral scanners (TRIOS 3, Primescan, and Virtuo Vivo). The IOS datasets were analyzed to assess trueness and precision using a two-way ANOVA and multiple-comparison tests with Bonferroni corrections (α = 0.05). Results: Both the model type and the IOS significantly influenced trueness and precision. The interaction between the model type and the IOS was found to be statistically significant (trueness: p = 0.001; precision: p = 0.005). The highest trueness was observed in the ACII model scanned with TRIOS 3 and Primescan. TRIOS 3 and Primescan also exhibited the highest precision in the ACII model. For Virtuo Vivo, there were no significant differences among the models (p = 0.48). Conclusions: Although intraoral scanners (IOSs) demonstrated significant differences in trueness when used in completely and partially edentulous models with maxillary defects, these differences may be considered clinically insignificant. Full article
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10 pages, 1359 KiB  
Article
Evaluation of External Apical Root Resorption in Cases with Extraction and Non-Extraction Fixed Orthodontic Treatment
by Ramazan Berkay Peker and Pamir Meriç
Diagnostics 2024, 14(20), 2338; https://doi.org/10.3390/diagnostics14202338 - 21 Oct 2024
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Abstract
Objective: The objective of this study was to evaluate external apical root resorption (EARR) in cases with extraction and non-extraction fixed orthodontic treatment. Methods: Ninety subjects were included in this study. The patients were divided into two groups: 43 with extraction treatment and [...] Read more.
Objective: The objective of this study was to evaluate external apical root resorption (EARR) in cases with extraction and non-extraction fixed orthodontic treatment. Methods: Ninety subjects were included in this study. The patients were divided into two groups: 43 with extraction treatment and 47 with non-extraction orthodontic treatment. EARR was measured using the crown-to-root ratio of the maxillary and mandibular incisors and canines on panoramic radiographs taken at the beginning (T0) and end of the treatment (T1). The Bonferroni corrected Z test was used for multiple comparisons. Results: There were 24 (55.8%) individuals in the extraction group and 12 (25.5%) in the non-extraction group, with a minimum of one tooth with severe resorption. There was no resorption in 0% of individuals in the extraction group and five (10.6%) individuals in the non-extraction group. There was a statistically significant correlation between the groups and the degree of resorption (p = 0.008). When the maxillary and mandibular teeth in the extraction group were compared, a significant difference was found in all degrees of resorption except for mild resorption. Conclusions: There was a significant difference in EARR between the extraction and non-extraction treatment groups, with maxillary incisors showing more resorption in the extraction treatment. Full article
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18 pages, 7920 KiB  
Article
Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition
by Yanlin Wang, Gang Li, Xinyue Zhang, Yue Wang, Zhenhao Zhang, Jupeng Li, Junqi Ma and Linghang Wang
Diagnostics 2024, 14(18), 2080; https://doi.org/10.3390/diagnostics14182080 - 20 Sep 2024
Viewed by 724
Abstract
Objectives: During deep learning model training, it is essential to consider the balance among the effects of sample size, actual resources, and time constraints. Single-arm objective performance criteria (OPC) was proposed to determine the optimal positive sample size for training deep learning [...] Read more.
Objectives: During deep learning model training, it is essential to consider the balance among the effects of sample size, actual resources, and time constraints. Single-arm objective performance criteria (OPC) was proposed to determine the optimal positive sample size for training deep learning models in caries recognition. Methods: An expected sensitivity (PT) of 0.6 and a clinically acceptable sensitivity (P0) of 0.5 were applied to the single-arm OPC calculation formula, yielding an optimal training set comprising 263 carious teeth. U-Net, YOLOv5n, and CariesDetectNet were trained and validated using clinically self-collected cone-beam computed tomography (CBCT) images that included varying quantities of carious teeth. To assess performance, an additional dataset was utilized to evaluate the accuracy of caries detection by both the models and two dental radiologists. Results: When the number of carious teeth reached approximately 250, the models reached the optimal performance levels. U-Net demonstrated superior performance, achieving accuracy, sensitivity, specificity, F1-Score, and Dice similarity coefficients of 0.9929, 0.9307, 0.9989, 0.9590, and 0.9435, respectively. The three models exhibited greater accuracy in caries recognition compared to dental radiologists. Conclusions: This study demonstrated that the positive sample size of CBCT images containing caries was predictable and could be calculated using single-arm OPC. Full article
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