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Emerging Technologies for Dental Imaging
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Emerging Technologies for Dental Imaging

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Dentistry, Oral Surgery and Oral Medicine".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 3917

Special Issue Editor

Dr. Egon Burian

E-Mail Website
Guest Editor
1. Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
2. Institute of Diagnostic and Interventional Radiology, Cantonal Hospital Frauenfeld, Frauenfeld, Switzerland
3. Institute of Diagnostic and Interventional Radiology, University Hospital of Ulm, Ulm, Germany
Interests: radiology; neuroradiology; body composition; spine imaging; head and neck radiology; dental MRI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue, we would like to cover the rapidly evolving field of dental imaging with a special focus on 3D imaging techniques like magnetic resonance imaging (MRI) or cone beam computed tomography (CBCT). The data on potential implementation fields of dental MRI like periodontology, oral surgery, orthodontics, and conservative dentistry have been growing for the past few years. Within this Special Issue, original research and review articles covering the advantages of novel MRI sequences while also expanding the evidence on CBCT in different clinical settings are particularly welcome, along with articles covering new artificial intelligence-driven techniques like pathology detection or the possibility of using large language models in dental imaging.

Dr. Egon Burian
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. Journal of Clinical Medicine 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

  • dental imaging
  • dental MRI
  • caries
  • periodontitis
  • orthodontics
  • oral surgery
  • craniomaxillofacial surgery

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Published Papers (3 papers)

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Research

11 pages, 889 KiB  
Article
Prediction of a Cephalometric Parameter and Skeletal Patterns from Lateral Profile Photographs: A Retrospective Comparative Analysis of Regression Convolutional Neural Networks
by Shota Ito, Yuichi Mine, Shiho Urabe, Yuki Yoshimi, Shota Okazaki, Mizuho Sano, Yuma Koizumi, Tzu-Yu Peng, Naoya Kakimoto, Takeshi Murayama and Kotaro Tanimoto
J. Clin. Med. 2024, 13(21), 6346; https://doi.org/10.3390/jcm13216346 - 23 Oct 2024
Viewed by 826
Abstract
Background/Objectives: Cephalometric analysis has a pivotal role in the quantification of the craniofacial skeletal complex, facilitating the diagnosis and management of dental malocclusions and underlying skeletal discrepancies. This study aimed to develop a deep learning system that predicts a cephalometric skeletal parameter [...] Read more.
Background/Objectives: Cephalometric analysis has a pivotal role in the quantification of the craniofacial skeletal complex, facilitating the diagnosis and management of dental malocclusions and underlying skeletal discrepancies. This study aimed to develop a deep learning system that predicts a cephalometric skeletal parameter directly from lateral profile photographs, potentially serving as a preliminary resource to motivate patients towards orthodontic treatment. Methods: ANB angle values and corresponding lateral profile photographs were obtained from the medical records of 1600 subjects (1039 female and 561 male, age range 3 years 8 months to 69 years 1 month). The lateral profile photographs were randomly divided into a training dataset (1250 images) and a test dataset (350 images). Seven regression convolutional neural network (CNN) models were trained on the lateral profile photographs and measured ANB angles. The performance of the models was assessed using the coefficient of determination (R2) and mean absolute error (MAE). Results: The R2 values of the seven CNN models ranged from 0.69 to 0.73, and the MAE values ranged from 1.46 to 1.53. Among the seven models, InceptionResNetV2 showed the highest success rate for predictions of ANB angle within 1° of range and the highest performance in skeletal class prediction, with macro-averaged accuracy, precision, recall, and F1 scores of 73.1%, 78.5%, 71.1%, and 73.0%, respectively. Conclusions: The proposed deep CNN models demonstrated the ability to predict a cephalometric skeletal parameter directly from lateral profile photographs, with 71% of predictions being within 2° of accuracy. This level of accuracy suggests potential clinical utility, particularly as a non-invasive preliminary screening tool. The system’s ability to provide reasonably accurate predictions without radiation exposure could be especially beneficial for initial patient assessments and may enhance efficiency in orthodontic workflows. Full article
(This article belongs to the Special Issue Emerging Technologies for Dental Imaging)
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17 pages, 2734 KiB  
Article
Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics
by Wojciech Kazimierczak, Grzegorz Gawin, Joanna Janiszewska-Olszowska, Marta Dyszkiewicz-Konwińska, Paweł Nowicki, Natalia Kazimierczak, Zbigniew Serafin and Kaan Orhan
J. Clin. Med. 2024, 13(13), 3733; https://doi.org/10.3390/jcm13133733 - 26 Jun 2024
Viewed by 1805
Abstract
Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: [...] Read more.
Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph. This study involved a retrospective analysis of lateral cephalograms from a single orthodontic center. Automated CA was performed using the AI programs, focusing on common parameters defined by Downs, Ricketts, and Steiner. Repeatability was tested through 50 randomly reanalyzed cases by each software. Statistical analyses included intraclass correlation coefficients (ICC3) for agreement and the Friedman test for concordance. Results: One hundred twenty-four cephalograms were analyzed. High agreement between the AI systems was noted for most parameters (ICC3 > 0.9). Notable differences were found in the measurements of angle convexity and the occlusal plane, where discrepancies suggested different methodologies among the programs. Some analyses presented high variability in the results, indicating errors. Repeatability analysis revealed perfect agreement within each program. Conclusions: AI-driven cephalometric analysis tools demonstrate a high potential for reliable and efficient orthodontic assessments, with substantial agreement in repeated analyses. Despite this, the observed discrepancies and high variability in part of analyses underscore the need for standardization across AI platforms and the critical evaluation of automated results by clinicians, particularly in parameters with significant treatment implications. Full article
(This article belongs to the Special Issue Emerging Technologies for Dental Imaging)
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14 pages, 5743 KiB  
Article
Reliability of the AI-Assisted Assessment of the Proximity of the Root Apices to Mandibular Canal
by Wojciech Kazimierczak, Natalia Kazimierczak, Kamila Kędziora, Marta Szcześniak and Zbigniew Serafin
J. Clin. Med. 2024, 13(12), 3605; https://doi.org/10.3390/jcm13123605 - 20 Jun 2024
Viewed by 995
Abstract
Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). Methods: This study involved 57 patients aged 18–30 whose [...] Read more.
Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). Methods: This study involved 57 patients aged 18–30 whose CT scans were analyzed by both AI and human experts. The primary aim was to measure the closest distance between the MC and RAs and to assess the AI tool’s diagnostic performance. The results indicated significant variability in RA-MC distances, with third molars showing the smallest mean distances and first molars the greatest. Diagnostic accuracy metrics for the AI tool were assessed at three thresholds (0 mm, 0.5 mm, and 1 mm). Results: The AI demonstrated high specificity but generally low diagnostic accuracy, with the highest metrics at the 0.5 mm threshold with 40.91% sensitivity and 97.06% specificity. Conclusions: This study underscores the limited potential of tested AI programs in reducing iatrogenic damage to the inferior alveolar nerve (IAN) during dental procedures. Significant differences in RA-MC distances between evaluated teeth were found. Full article
(This article belongs to the Special Issue Emerging Technologies for Dental Imaging)
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