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Medical Imaging Diagnosis of Oral and Maxillofacial Diseases

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

Deadline for manuscript submissions: closed (31 May 2026) | Viewed by 6595

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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

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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
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Prof. Dr. Erwin Berkhout

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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 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-anonymized 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 (6 papers)

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Research

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17 pages, 1141 KB

Open AccessArticle

Impact of Imaging Modality on AI-Based Detection of Incidental Maxillary Sinus Pathology: Comparison of Panoramic Radiography and CBCT

by Anna Lackowska, Natalia Kazimierczak, Natalia Chwarścianek, Nora Sultani, Zbigniew Serafin and Wojciech Kazimierczak

Diagnostics 2026, 16(11), 1667; https://doi.org/10.3390/diagnostics16111667 - 28 May 2026

Cited by 1 | Viewed by 510

Abstract

Background/Objectives: The objective of our study was to compare the diagnostic performance of a popular, commercial dental artificial intelligence (AI) platform (Diagnocat, DGNCT LLC, Miami, FL, USA) for detecting maxillary sinus abnormalities on paired panoramic radiographs (OPG) and cone-beam computed tomography (CBCT) acquired in the same patients, and to examine whether lesion conspicuity predicts correct AI decisions. Methods: In this retrospective paired study, 166 patients contributed 332 maxillary sinuses with OPG and CBCT performed ≤30 days apart. The reference standard was consensus CBCT reading by two observers with third-reader arbitration. The index test was the AI’s sinus-level binary output (any abnormality). Accuracy, precision, recall, and F1 score were estimated with patient-clustered 95% bootstrap CIs; secondary analyses assessed category-specific performance and the effect of mucosal thickness and polyp/cyst volume. Results: Our evaluation showed that the platform’s performance depended on modality. On CBCT, the accuracy was 69.88% (64.76–74.70%), precision was 87.83% (81.58–93.33%), recall was 54.01% (46.74–61.17%), and F1 score was 66.89% (60.34–72.84%). On OPG, the accuracy was 50.6% (44.58–55.41%), precision was 67.80% (55.38–79.66%), recall was 21.39% (15.62–27.32%), and F1 score 32.52% (24.79–39.69%). On CBCT, higher mucosal thickness and larger polyp/cyst volume strongly predicted correct AI calls; no such effect was seen with OPG. Conclusions: In conclusion, the evaluated AI showed high precision but only moderate recall on CBCT and unreliable performance on OPG. Outputs must be interpreted by a professional; AI alerts on OPG should not guide management without CBCT confirmation. Full article

(This article belongs to the Special Issue Medical Imaging Diagnosis of Oral and Maxillofacial Diseases)

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12 pages, 1191 KB

Open AccessArticle

The Influence of Panoramic Radiograph Quality on the Accuracy of AI-Based Tooth Detection

by Julien Issa, Reinier Hoogeveen, Marta Dyszkiewicz-Konwińska and Erwin Berkhout

Diagnostics 2026, 16(11), 1650; https://doi.org/10.3390/diagnostics16111650 - 27 May 2026

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Abstract

Objectives: This study aimed to evaluate the influence of panoramic radiograph quality on the performance of an artificial intelligence (AI)-based tooth detection system and to identify specific image quality criteria associated with detection accuracy. Methods: A total of 424 panoramic radiographs were retrospectively selected from a clinical database. Radiographic quality was assessed using a modified Clinical Image Evaluation Chart, including criteria related to bite block presence, anteroposterior positioning, occlusal plane curvature, patient movement, anatomical coverage, overlapping contact points, air gap, contrast, cervical spine overlap, symmetry of the ascending mandibular ramus, and the number of visible teeth. Automated tooth detection was performed using a convolutional neural network based on the Mask R-CNN architecture (SynbrAIn, Italy). AI detection outputs were validated against expert human evaluation. Spearman’s rank correlation analyses were conducted to assess associations between individual image quality criteria and the number of AI detection errors per radiograph. Results: Significant negative associations were observed between AI detection errors and the number of visible teeth (ρ = −0.311, p < 0.001), presence of a bite block (ρ = −0.248, p < 0.001), reduced patient movement (ρ = −0.204, p < 0.001), correct anteroposterior positioning (ρ = −0.165, p < 0.001), and overall image quality score (ρ = −0.120, p = 0.010). In contrast, the presence of an air gap above the anterior teeth (ρ = 0.099, p = 0.042) and overlapping contact points (ρ = 0.122, p = 0.012) were positively associated with increased detection errors. No significant associations were identified for occlusal plane curvature, contrast, cervical spine overlap, anatomical coverage, or mandibular ramus symmetry. Overall, the AI system was more sensitive to indicators of anatomical completeness and patient positioning than to minor radiographic imperfections. Conclusions: Panoramic radiograph quality, particularly indicators of anatomical completeness and patient positioning, is associated with the performance of AI-based tooth detection. While the AI system demonstrated robustness to common image quality variations, adherence to standardized acquisition protocols remains important to minimize detection errors. Full article

(This article belongs to the Special Issue Medical Imaging Diagnosis of Oral and Maxillofacial Diseases)

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15 pages, 1632 KB

Open AccessArticle

Reliability of AI-Automated and Semiautomated Upper Airway Volume Segmentation

by Natalia Chwarścianek, Natalia Kazimierczak, Zbigniew Serafin and Wojciech Kazimierczak

Diagnostics 2026, 16(7), 1105; https://doi.org/10.3390/diagnostics16071105 - 7 Apr 2026

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Abstract

Background/Objective: To evaluate the reliability, diagnostic accuracy and time efficiency of an artificial intelligence (AI)-automated method (CephX) and a semiautomated method (INVIVO) for upper airway segmentation, the manual digital method (ITK-SNAP) was used as the reference standard. Methods: This retrospective study analyzed cone-beam computed tomography (CBCT) scans from 133 patients. The upper airway volume and narrowest cross-sectional area were measured via the three methods. Reliability and repeatability were assessed via the intraclass correlation coefficient (ICC). The time required for each segmentation method was also recorded and compared. Results: Both the AI-automated (ICC = 0.945) and semiautomated (ICC = 0.992) methods demonstrated excellent reliability for total volume measurements compared with the manual reference. For the narrowest area, the automated method showed excellent agreement (ICC = 0.943), whereas the semiautomated method showed good agreement (ICC = 0.868). All methods demonstrated excellent intrareader repeatability (ICC > 0.95) and high test–retest reliability. The AI-automated method was significantly more time-efficient, requiring less than 30 s per analysis, compared with 161.4 s for the semiautomated method and 336.6 s for the manual method. Conclusions: AI-automated and semiautomated segmentation methods are reliable and accurate alternatives to manual upper airway analysis. The AI-based approach offers a substantial advantage in time efficiency, making it a valuable tool for clinical practice. Full article

(This article belongs to the Special Issue Medical Imaging Diagnosis of Oral and Maxillofacial Diseases)

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15 pages, 2626 KB

Open AccessArticle

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 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

Open AccessArticle

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

Cited by 6 | Viewed by 1653

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 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

Open AccessReview

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 1622

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 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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