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Dentistry Journal
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Artificial Intelligence Technology in Periodontology and Implantology: Development and Prospect
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Artificial Intelligence Technology in Periodontology and Implantology: Development and Prospect

A special issue of Dentistry Journal (ISSN 2304-6767). This special issue belongs to the section "Digital Technologies".

Deadline for manuscript submissions: 15 January 2027 | Viewed by 867

Editor

Dr. Yaniv Mayer

E-Mail Website
Guest Editor
1. The Ruth and Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa 3109601, Israel
2. Department of Periodontology, Rambam Health Care Campus, Haifa 3525408, Israel
Interests: periodontology; peri-implantitis; digital dentistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence is rapidly reshaping the landscape of periodontal and implant dentistry, offering unprecedented opportunities for early diagnosis, treatment planning, and predictive outcomes. This Special Issue aims to bring together original research, systematic reviews, and clinical perspectives that explore the integration of AI-driven technologies across periodontology and implantology. Topics of interest include, but are not limited to, machine learning and deep learning approaches for radiographic detection of periodontal bone loss and peri-implant pathology, AI-assisted implant planning and guided surgery, natural language processing for clinical record analysis, and predictive modeling for treatment prognosis and implant survival. We also welcome studies addressing the ethical, regulatory, and practical challenges of implementing AI tools in clinical periodontal and implant practice. By compiling cutting-edge contributions from clinicians, researchers, and data scientists, this Special Issue seeks to define the current state of the art and chart future directions for intelligent, evidence-based care in these interconnected disciplines.

Dr. Yaniv Mayer
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Dentistry Journal is an international peer-reviewed open access monthly 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 2000 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
  • periodontal diagnosis
  • implantology
  • machine learning
  • predictive modeling
  • deep learning
  • large language model

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Published Papers (2 papers)

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Review

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14 pages, 1815 KB  
Review
Artificial Intelligence in Periodontology: From Automated Diagnosis to Prediction and Clinical Decision Support—A Narrative Review
by Marco M. Herz and Valentin Bartha
Dent. J. 2026, 14(8), 531; https://doi.org/10.3390/dj14080531 - 20 Aug 2026
Abstract
Background/Objectives: We aimed to evaluate the methodological quality, translational limitations, and clinical applicability of current artificial intelligence (AI) applications in periodontology and to propose a framework for validated prediction and decision support. Methods: A structured narrative review based on a targeted, [...] Read more.
Background/Objectives: We aimed to evaluate the methodological quality, translational limitations, and clinical applicability of current artificial intelligence (AI) applications in periodontology and to propose a framework for validated prediction and decision support. Methods: A structured narrative review based on a targeted, non-systematic literature search was conducted using PubMed and cross-disciplinary sources (January 2015–April 2026). Evidence from primary studies, systematic reviews, and methodological guidance for AI prediction models and clinical decision-support systems was synthesized with a focus on clinical applicability. Results: Current periodontal AI research is dominated by retrospective studies focusing on radiographic phenotyping, where deep learning models demonstrate promising diagnostic performance for detecting and quantifying periodontal bone loss. However, substantial limitations persist, including heterogeneous endpoints, inconsistent reporting, limited external validation, and insufficient calibration assessment. Importantly, there is little evidence that AI-based tools improve clinical decision-making or patient-relevant outcomes. Emerging work on prognostic modeling and multimodal data integration highlights the potential for individualized periodontal risk prediction but remains undervalidated, with limited evidence for clinical implementation. Conclusions: Although AI-based models show promising diagnostic performance, translational progress in periodontology is currently limited by insufficient validation and the lack of evidence for clinical utility. Future research should prioritize clinically actionable prediction models, robust external validation, and prospective evaluation of AI-supported decision-making within real-world periodontal care pathways. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology in Periodontology and Implantology: Development and Prospect)
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15 pages, 3064 KB  
Systematic Review
Diagnostic Performance of Artificial Intelligence Models for Periodontitis Disease Detection Using Panoramic Radiographs: A Systematic Review
by Khalid Almutairi, Tariq Almanseer, Enrique España Guerrero, Antonio José España and Gerardo Moreu
Dent. J. 2026, 14(7), 416; https://doi.org/10.3390/dj14070416 - 7 Jul 2026
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Abstract
Background/Objectives: Periodontitis is a highly prevalent inflammatory disease and a major cause of tooth loss worldwide. Accurate diagnosis requires integration of clinical and radiographic findings, but interpretation of panoramic radiographs is subject to variability. Artificial intelligence (AI) has emerged as a promising [...] Read more.
Background/Objectives: Periodontitis is a highly prevalent inflammatory disease and a major cause of tooth loss worldwide. Accurate diagnosis requires integration of clinical and radiographic findings, but interpretation of panoramic radiographs is subject to variability. Artificial intelligence (AI) has emerged as a promising adjunct for radiographic assessment. This systematic review evaluated the diagnostic performance of AI-based models for detecting periodontitis using panoramic radiographic images. Methods: A systematic search of PubMed, Scopus, and Web of Science identified studies published between 1 January 2015 and 1 March 2026. Eligible studies assessed AI models for periodontitis detection on panoramic radiographs and used either clinically confirmed periodontal diagnosis or expert radiographic annotation as the reference standard. Data extraction and quality assessment were performed independently by two reviewers using the QUADAS-2 tool. Owing to heterogeneity in AI architectures, datasets, and outcome measures, a narrative synthesis was conducted. Results: Nine studies met the inclusion criteria, comprising more than 20,000 radiographs. AI models included convolutional neural networks (CNNs), segmentation-based systems, and hybrid architectures. Sensitivity ranged from 0.795 to 1.00, specificity from 0.784 to 0.99, and AUC values from 0.843 to 0.967. Studies using clinical periodontal diagnosis as the reference standard generally reported lower performance than those relying solely on expert annotation. Only four studies performed external validation, and dataset sizes varied widely. One study combining panoramic and periapical radiographs showed moderate diagnostic performance. Conclusions: AI-based diagnostic models demonstrate promising performance for detecting periodontitis on panoramic radiographs, with several studies reporting high sensitivity and AUC values. However, heterogeneity in reference standards, limited external validation, and inconsistent dataset quality restrict generalizability. AI should be considered an adjunct to, rather than a replacement for, comprehensive clinical periodontal examination. Standardized datasets and robust external validation are needed to support clinical implementation. Full article
(This article belongs to the Special Issue Artificial Intelligence Technology in Periodontology and Implantology: Development and Prospect)
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