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Diagnostics
Special Issues
Medical Imaging Diagnosis of Oral and Maxillofacial Diseases
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Medical Imaging Diagnosis of Oral and Maxillofacial Diseases

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 2247

Special Issue Editors

Dr. Julien Issa

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Guest Editor
Department of Oral Radiology & Digital Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam & University of Amsterdam, 1081 HV Amsterdam, The Netherlands
Interests: oral and maxillofacial diagnostics; CBCT and advanced digital imaging; artificial intelligence in dental radiology; digital dentistry; AI regulation in clinical practice
Prof. Dr. Marta Dyszkiewicz-Konwińska

E-Mail Website
Guest Editor
Department of Diagnostics, Poznan University of Medical Sciences, 60-812 Poznań, Poland
Interests: oral and maxillofacial diagnostics; CBCT and advanced digital imaging; Artificial intelligence in dental radiology; tissue regeneration; dental biomaterials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Erwin Berkhout

E-Mail Website
Guest Editor
Department of Oral Radiology & Digital Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), Vrije Universiteit Amsterdam & University of Amsterdam, 1081 HV Amsterdam, The Netherlands
Interests: oral and maxillofacial radiology; CBCT and advanced digital imaging; radiation protection; Artificial intelligence in dental radiology; digital transformation in dental education

Special Issue Information

Dear Colleagues,

Advances in medical imaging have transformed the diagnosis, treatment planning, and follow-up of oral and maxillofacial diseases. From conventional radiography to cone-beam computed tomography (CBCT), dental-dedicated magnetic resonance imaging (ddMRI), and emerging artificial intelligence (AI)–driven approaches, imaging is central to diagnosis and treatment planning. This Special Issue, “Medical Imaging Diagnosis of Oral and Maxillofacial Diseases”, aims to bring together high-quality research and reviews addressing innovations and challenges in diagnostic imaging. The focus is on the integration of cutting-edge imaging modalities and computational methods for accurate detection, characterization, and risk assessment of diseases affecting the jaws, teeth, temporomandibular joint, salivary glands, and associated structures. We welcome submissions on clinical applications, imaging protocols, AI algorithms, 3D reconstruction, and interdisciplinary approaches that enhance diagnostic precision and patient care. The purpose is to highlight current trends and foster future directions in oral and maxillofacial diagnostic imaging.

Dr. Julien Issa
Prof. Dr. Marta Dyszkiewicz-Konwińska
Prof. Dr. Erwin Berkhout
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics 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

  • oral radiology
  • digital dentistry
  • dental imaging
  • diagnosis
  • treatment planning

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Published Papers (3 papers)

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Research

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15 pages, 2626 KB  
Article
Integration of Photon-Counting CT into the Surgical Workflow of Complex Maxillofacial Reconstruction: A Pilot Feasibility Study
by Ioanna Kalaitsidou, Matias Maissen, Florian Dammann, Christian Schedeit, Daniel Jan Toneatti and Benoît Schaller
Diagnostics 2026, 16(6), 876; https://doi.org/10.3390/diagnostics16060876 - 16 Mar 2026
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Abstract
Background/Objectives: Virtual surgical planning (VSP) and CAD/CAM technologies have revolutionized complex maxillofacial reconstruction. While high-resolution imaging is critical for these workflows, the specific clinical impact of photon-counting computed tomography (PCCT) remains to be fully established. This prospective pilot study evaluates the feasibility and [...] Read more.
Background/Objectives: Virtual surgical planning (VSP) and CAD/CAM technologies have revolutionized complex maxillofacial reconstruction. While high-resolution imaging is critical for these workflows, the specific clinical impact of photon-counting computed tomography (PCCT) remains to be fully established. This prospective pilot study evaluates the feasibility and clinical utility of integrating PCCT into the preoperative planning and surgical workflow of complex maxillofacial reconstructive cases. Methods: This feasibility study included ten patients requiring complex maxillofacial reconstruction with microvascular free flaps. All underwent preoperative imaging with photon-counting CT. Primary endpoints included clinical assessment of osseous invasion, reliability of donor-site vascular mapping from a single acquisition, and compatibility of PCCT datasets with VSP/CAD-CAM platforms. Secondary endpoints included resection margin status, flap survival, and short-term oncologic outcomes. Results: PCCT provided high-resolution visualization of cortical and medullary bone, enabling detailed assessment of tumor-related osseous involvement. In selected cases, findings supported refinement of resection planning when prior imaging had been inconclusive. Spectral reconstructions reduced metal artifacts and facilitated precise segmentation for multi-segment osteotomies. Donor-site vascular anatomy was successfully evaluated within the same scan, supporting operative planning without additional imaging. PCCT datasets were fully compatible with the virtual surgical planning (VSP) software used in this study (CMX Portal, version 2.6.1158, Medartis AG, Basel, Switzerland; or ProPlan CMF, version 5.7.8.025, Materialise NV, Leuven, Belgium) in all cases (100%). Reconstruction was completed successfully in all patients, with 100% flap survival and R0 margins in all malignant cases. No technical failures occurred during imaging transfer or CAD/CAM fabrication. Conclusions: The integration of PCCT into the surgical workflow proved technically feasible and clinically impactful. This pilot data supports its potential to enhance surgical precision and preoperative planning in complex jaw reconstruction. Full article
(This article belongs to the Special Issue Medical Imaging Diagnosis of Oral and Maxillofacial Diseases)
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13 pages, 2512 KB  
Article
AI-Based Detection of Dental Features on CBCT: Dual-Layer Reliability Analysis
by Natalia Kazimierczak, Nora Sultani, Natalia Chwarścianek, Szymon Krzykowski, Zbigniew Serafin, Aleksandra Ciszewska and Wojciech Kazimierczak
Diagnostics 2025, 15(24), 3207; https://doi.org/10.3390/diagnostics15243207 - 15 Dec 2025
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Abstract
Background/Objectives: Artificial intelligence (AI) systems may enhance diagnostic accuracy in cone-beam computed tomography (CBCT) analysis. However, most validations focus on isolated tooth-level tasks rather than clinically meaningful full-mouth assessment outcomes. To evaluate the diagnostic accuracy of a commercial AI platform for detecting dental [...] Read more.
Background/Objectives: Artificial intelligence (AI) systems may enhance diagnostic accuracy in cone-beam computed tomography (CBCT) analysis. However, most validations focus on isolated tooth-level tasks rather than clinically meaningful full-mouth assessment outcomes. To evaluate the diagnostic accuracy of a commercial AI platform for detecting dental treatment features on CBCT images at both tooth and full-scan levels. Methods: In this retrospective single-center study, 147 CBCT scans (4704 tooth positions) were analyzed. Two experienced readers annotated treatment features (missing teeth, fillings, endodontic treatments, crowns, pontics, orthodontic appliances, implants), and consensus served as the reference. Anonymized datasets were processed by a cloud-based AI system (Diagnocat Inc., San Francisco, CA, USA). Diagnostic metrics—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score—were calculated with 95% patient-clustered bootstrap confidence intervals. A “Perfect Agreement” criterion defined full-scan level success as an entirely error-free full-mouth report. Results: Tooth-level AI performance was excellent, with accuracy exceeding 99% for most categories. Sensitivity was highest for missing teeth (99.3%) and endodontic treatments (99.0%). Specificity and NPV exceeded 98.5% and 99.7%, respectively. Full-scan level Perfect Agreement was achieved in 82.3% (95% CI: 76.2–88.4%), with errors concentrated in teeth presenting multiple co-existing findings. Conclusions: The evaluated AI platform demonstrates near-perfect accuracy in detecting isolated dental features but moderate reliability in generating complete full-mouth reports. It functions best as an assistive diagnostic tool, not as an autonomous system. Full article
(This article belongs to the Special Issue Medical Imaging Diagnosis of Oral and Maxillofacial Diseases)
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Review

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22 pages, 829 KB  
Review
Use of Artificial Intelligence for Diagnosing Oral Mucosa Conditions: A Review
by Bianka Andrzejczak, Aleksandra Diedul, Anna Szczepankiewicz, Piotr Trojanowski, Antoni Skrzypczak, Anna Bączkiewicz, Hanna Szymańska, Marzena Liliana Wyganowska and Zuzanna Ślebioda
Diagnostics 2026, 16(2), 365; https://doi.org/10.3390/diagnostics16020365 - 22 Jan 2026
Viewed by 550
Abstract
Artificial Intelligence (AI) is a computer science that focuses on developing systems and machines capable of performing tasks that typically require human cognitive abilities. It has widespread applications in medical diagnostics. Its use has led to rapid advancements in diagnostic methodology, enabling the [...] Read more.
Artificial Intelligence (AI) is a computer science that focuses on developing systems and machines capable of performing tasks that typically require human cognitive abilities. It has widespread applications in medical diagnostics. Its use has led to rapid advancements in diagnostic methodology, enabling the analysis of large datasets. The major applications of AI in medical diagnostics include personalized treatment based on patient genetics, preventive measures, and medical image analysis. AI is employed to analyse genomic data and biomarkers, aiding in the precise tailoring of therapies to individual patient needs. It could also be employed in modern dentistry in the near future, helping to achieve higher efficiency and accuracy in diagnosis and treatment planning. AI may be utilized in screening for oral mucosa lesions and to discriminate between oral potentially malignant disorders and cancers from benign lesions. The potential advantages of AI include high speed and accuracy in the diagnostic process, as well as relatively low costs. The aim of this review was to present the potential applications of AI methods in the diagnosis of selected mucocutaneous diseases. A literature review focuses on oral lichen planus, recurrent aphthous stomatitis, and oral and laryngeal leukoplakia. Full article
(This article belongs to the Special Issue Medical Imaging Diagnosis of Oral and Maxillofacial Diseases)
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