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Clinical Research and Application of Artificial Intelligence and Deep Learning in Dentistry and Oral and Maxillofacial Surgery

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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 September 2026 | Viewed by 1157

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Editors

Dr. Babak Saravi

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Guest Editor

Department of Oral, Maxillofacial and Facial Plastic Surgery, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
Interests: imaging; biostatistics; artificial intelligence; deep learning; machine learning; oral and maxillofacial surgery; dentistry
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Dr. Julian Lommen

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Co-Guest Editor

Dr. Lara Katharina Franziska Schorn

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Co-Guest Editor

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and deep learning are rapidly transforming dentistry and oral and maxillofacial surgery (OMFS), offering unprecedented opportunities to enhance diagnostic accuracy, treatment planning, and patient outcomes. Recent advances in convolutional neural networks and large language models have enabled automated detection of caries, periapical lesions, cysts, and tumors on radiographic images, as well as precise cephalometric landmark identification and virtual surgical planning for orthognathic procedures. Despite this remarkable progress, critical challenges remain: limited dataset diversity, insufficient external validation, a lack of prospective multicenter studies, regulatory uncertainty, and unresolved ethical concerns regarding algorithmic transparency and patient data privacy. This Special Issue aims to consolidate current evidence and stimulate high-quality original research at the intersection of AI/deep learning and clinical dentistry/OMFS. We welcome contributions addressing imaging diagnostics, pathology detection, implantology, orthodontics, surgical outcome prediction, natural language processing in clinical workflows, and the development of robust, generalizable AI frameworks for real-world clinical translation. We invite researchers, clinicians, and interdisciplinary teams worldwide to submit original articles, reviews, and clinical studies that advance the responsible integration of AI technologies into dental and maxillofacial practice.

Dr. Babak Saravi
Guest Editor

Dr. Julian Lommen
Dr. Lara Katharina Franziska Schorn
Co-Guest Editors

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Keywords

artificial intelligence
deep learning
machine learning
oral and maxillofacial surgery
dentistry
medical imaging
clinical decision support

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Published Papers (3 papers)

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26 pages, 2512 KB

Open AccessArticle

Diagnostic Performance of AI-Based Cloud Software Regarding the Detection of Endodontic Findings on CBCT: A Single-Centre Cross-Sectional Validation Study

by Maythem Al Fartousi, Arthur Buscot and Christian Ralf Gernhardt

J. Clin. Med. 2026, 15(12), 4839; https://doi.org/10.3390/jcm15124839 - 22 Jun 2026

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Abstract

Background/Objectives: The aim of the present investigation was to validate the diagnostic performance of the AI-based dental cloud software Diagnocat^® AIS (Version 1.0 (UDI: 860010268018), DGNCT LLC, Miami, FL, USA) regarding the detection possibilities of seven different endodontic findings on cone-beam computed tomography (CBCT) against a multi-rater consensus reference standard, and to characterize its calibration, threshold-optimized performance and clinical utility. Methods: 358 root-canal-treated teeth from 167 CBCT scans (167 patients) were retrospectively evaluated at a single private dental practice. From initially included 383 root-canal-treated teeth from 177 patients, 358 (93.5%) were recognized by the AI tool and entered the primary analysis. Two experienced dentists with a clinical focus on endodontics independently graded each tooth and disagreements were adjudicated by a senior expert. Seven different endodontic findings were evaluated: (i) apical (periapical) lesion; (ii) short root-canal filling (apical filling end >2 mm short of the radiographic apex); (iii) voids/lacunae in the root-canal filling; (iv) missed (un-instrumented/un-filled) canal; (v) overfilled root-canal filling (apical extrusion); (vi) apicoectomy (resected root apex with or without retrograde filling); and (vii) coronal restoration with a full-coverage crown. Diagnocat^® output was binarized at the manufacturer-fixed 0.50 probability threshold; sensitivity, specificity, predictive values, accuracy, area under the curve AUC (ROC), Cohen κ and Gwet AC1 were computed with 95% cluster-bootstrap confidence intervals (cluster = scan). Threshold optimization, probability calibration, GEE-based subgroup analyses, and decision-curve analysis were pre-specified. Results: Diagnostic performance varied by finding. AUCs were 0.984 for missed canal, 0.917 for overfilled root canal, 0.902 for short root filling, 0.893 for crown, 0.864 for apical lesion, 0.857 for apicoectomy and 0.761 for voids in the root filling. Apical-lesion sensitivity rose from 33.6% for sub-millimeter lesions to ≥80% for lesion measuring 1–5 mm. Re-tuning the decision threshold raised missed-canal sensitivity from 69.6% to 97.5%. Decision-curve analysis confirmed positive benefits for missed canal and root-filling-quality findings. Conclusions: The AI tool Diagnocat^® can be recommended as a focused screening adjunct in CBCT-based endodontic interpretation for missed canals, crowns, and gross root-filling-quality flaws. Sub-millimeter apical lesions and several less common findings (resorption, instrument fragment, retrograde filling) remain outside the reliable performance envelope of the current platform. Full article

(This article belongs to the Special Issue Clinical Research and Application of Artificial Intelligence and Deep Learning in Dentistry and Oral and Maxillofacial Surgery)

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19 pages, 285 KB

Open AccessArticle

Diagnostic Performance and Error Patterns of a Large Language Model and Neural Network in Periodontitis Classification: A Comparative Study

by Agata Ossowska, Aida Kusiak, Albert Camlet and Dariusz Świetlik

J. Clin. Med. 2026, 15(12), 4837; https://doi.org/10.3390/jcm15124837 - 22 Jun 2026

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Abstract

Background/Objectives: Periodontitis is a highly prevalent chronic disease requiring accurate diagnosis for effective treatment planning. Artificial intelligence (AI) has emerged as a potential tool to support clinical decision-making. This study aimed to compare the diagnostic performance and classification error patterns of a large language model (LLM) and a neural network (NN) in periodontitis classification according to the current staging and grading system. Methods: This retrospective study included 110 patients with periodontal disease. Clinical and demographic variables (age, sex, smoking status, number of teeth, API, BOP, PPD, and CAL) were analyzed. Reference diagnoses were established by two experts. Cases were evaluated using an LLM and a neural network. Model performance was assessed using accuracy, confusion matrices, and Cohen’s kappa coefficient, along with error analysis. Results: The LLM achieved 62% accuracy for stage and 63% for grade classification (κ = 0.48). The neural network showed higher performance, with 85% accuracy for stage and 79% for grade (κ = 0.79 and κ = 0.67, respectively). The LLM more often underestimated disease severity, whereas the neural network tended to overestimate progression. Differences between models were statistically significant (p < 0.0001). Conclusions: In this dataset and classification task, the task-specific neural network demonstrated higher diagnostic performance than the evaluated large language model. However, the findings should be interpreted in light of the fundamentally different training paradigms and intended applications of these AI systems. Further research is required to optimize and validate AI-based approaches for clinical use. Full article

(This article belongs to the Special Issue Clinical Research and Application of Artificial Intelligence and Deep Learning in Dentistry and Oral and Maxillofacial Surgery)

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16 pages, 1182 KB

Open AccessSystematic Review

Artificial Intelligence in the Radiological Diagnosis of Impacted Maxillary Canines: A Systematic Review

by Maciej Jedliński, Adam Jedliński, Gabriel Rostkowski, Joanna Janiszewska-Olszowska and Marta Mazur

J. Clin. Med. 2026, 15(9), 3373; https://doi.org/10.3390/jcm15093373 - 28 Apr 2026

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Abstract

Objectives: The aim of this systematic review was to evaluate whether artificial intelligence systems improve the diagnosis and localization assessment of impacted canines in radiological imaging. Methods: A systematic literature search was conducted across four electronic databases (MEDLINE/PubMed, Scopus, Embase, and Web of Science) for studies published after 2020, with no language restrictions. Eligible studies were comparative studies involving human subjects that evaluated AI-based systems against experienced clinicians or accepted radiological reference standards for the detection and localization of impacted canines. The risk of bias and applicability were assessed using the adapted QUADAS-3 tool. The review protocol was prospectively registered in PROSPERO (CRD42023487320). Results: The search strategy identified 110 records. After the removal of 41 duplicates, 69 articles were screened by title and abstract. Seventeen studies underwent full-text evaluation, and eight studies met the inclusion criteria and were included in the qualitative synthesis. Across the included studies, the overall risk of bias was considered high, primarily due to retrospective study design and limitations in reporting of methodological procedures. Conclusions: The available evidence does not provide high-quality studies addressing the studied issue. AI appears to yield more favorable results in CBCT analysis when compared to panoramic radiographs. However, this observation should be interpreted with caution, because the compared studies did not address the same clinical task, since these radiographs were taken in different clinical situations. Further well-designed studies with standardized datasets and external validation are required to better define the potential of artificial intelligence in orthodontic radiological diagnostics. Full article

(This article belongs to the Special Issue Clinical Research and Application of Artificial Intelligence and Deep Learning in Dentistry and Oral and Maxillofacial Surgery)

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Clinical Research and Application of Artificial Intelligence and Deep Learning in Dentistry and Oral and Maxillofacial Surgery

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