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Artificial Intelligence (AI) in Dental Clinical Practice

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: 20 January 2026 | Viewed by 595

Special Issue Editors


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Guest Editor
Department of Orthodontics and Dentofacial Orthopedics, Tokushima University Graduate School of Biomedical Sciences, Tokushima 770-8504, Japan
Interests: biomechanics of temporomandibular joint; temporomandibular disorders; tissue engineering; therapeutic ultrasound; clinical orthodontics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Orthodontics and Dentofacial Orthopedics, Tokushima University Graduate School of Biomedical Sciences, Tokushima 770-8504, Japan
Interests: digital dentistry; AI; resin composite; clinical orthodontics

Special Issue Information

Dear Colleagues,

Background: Artificial intelligence (AI) has become increasingly integrated into various aspects of society, including healthcare. In dentistry, the adoption of AI technologies is gradually gaining recognition for their potential to enhance diagnostic accuracy, streamline clinical workflows, and support personalized treatment plans based on large-scale data analysis.

Aim and scope: This Special Issue aims to provide a scientific forum for exploring and critically evaluating the application of AI in dental clinical practice. We particularly emphasize evidence-based validation and the need to ensure transparency, reliability, and accuracy in AI-driven systems used for clinical examination, diagnosis, and treatment planning.

History: Although AI has already shown significant progress in general medicine—such as radiology and pathology—its implementation in dentistry remains in an early but rapidly evolving stage. The recent emergence of machine learning, deep learning, and advanced image-processing techniques has led to a dramatic increase in the number of AI tools, many of which still require robust clinical evaluation.

We encourage submissions that address both technological innovation and clinical relevance, particularly studies on automated diagnostic algorithms, AI-driven radiographic interpretation, and real-time clinical decision support. Contributions analyzing the ethical, legal, and social implications of using AI in dental settings are also highly welcome.

We welcome high-quality original research articles, comprehensive reviews, and detailed case studies that rigorously examine AI technologies across all dental specialties. Submissions should highlight the limitations of AI decision-making, the integrity and transparency of training datasets, and the essential role of clinician oversight in AI-assisted care.

Prof. Dr. Eiji Tanaka
Dr. Keiichiro Watanabe
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.

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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
  • dental diagnostics
  • machine learning
  • deep learning
  • digital dentistry
  • clinical decision support
  • diagnostic automation
  • data transparency
  • evidence-based dentistry

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Published Papers (1 paper)

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Research

9 pages, 6062 KB  
Article
Age Estimation from Lateral Cephalograms Using Deep Learning: A Pilot Study from Early Childhood to Older Adults
by Ryohei Tokinaga, Yuichi Mine, Yuki Yoshimi, Shota Okazaki, Shota Ito, Saori Takeda, Saki Ogawa, Tzu-Yu Peng, Naoya Kakimoto, Kotaro Tanimoto and Takeshi Murayama
J. Clin. Med. 2025, 14(19), 7084; https://doi.org/10.3390/jcm14197084 - 7 Oct 2025
Viewed by 320
Abstract
Background/Objectives: The purpose of this study is twofold: first, to construct and evaluate a deep-learning model for automated age estimation from lateral cephalograms spanning early childhood to older adulthood; and second, to determine whether sex-specific training improves predictive accuracy. Methods: This [...] Read more.
Background/Objectives: The purpose of this study is twofold: first, to construct and evaluate a deep-learning model for automated age estimation from lateral cephalograms spanning early childhood to older adulthood; and second, to determine whether sex-specific training improves predictive accuracy. Methods: This retrospective study examined 600 lateral cephalograms (ages 4–63 years; 300 female, 300 male). The images were randomly divided into five cross-validation folds, stratified by sex and age. An ImageNet-pretrained DenseNet-121 was employed for age regression. Three networks were trained: mixed-sex, female-only, and male-only. Performance was evaluated using mean absolute error (MAE) and the coefficient of determination (R2). Grad-CAM heatmaps quantified the contributions of six craniofacial regions. Duplicate patients were excluded to minimize sampling bias. Results: The mixed-sex model achieved an MAE of 2.50 ± 0.27 years, an R2 of 0.84 ± 0.04, the female-only model achieved an MAE of 3.04 ± 0.37 years and an R2 of 0.82 ± 0.04, and the male-only model achieved an MAE of 2.29 ± 0.27 years and an R2 of 0.83 ± 0.04. Grad-CAM revealed dominant activations over the frontal bone in the mixed-sex model; the occipital bone and cervical soft tissue in the female model; and the parietal bone in the male model. Conclusions: A DenseNet-121-based analysis of lateral cephalograms can provide a clinically relevant age estimation with an error margin of approximately ±2.5 years. Using male-only model slightly improves performance metrics, and careful attention to training data distribution is crucial for broad applicability. Our findings suggest a potential contribution to forensic age estimation, growth and development research, and support for unidentified deceased individuals when dental records are unavailable. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Dental Clinical Practice)
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