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Diagnostics
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Advances in Head and Neck and Oral Maxillofacial Radiology
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Advances in Head and Neck and Oral Maxillofacial Radiology

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3579

Special Issue Editor

Dr. Qiyong Hemis Ai

E-Mail Website
Guest Editor
Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
Interests: quantitative MRI; head and neck cancer; three-dimensional CT; dentomaxillofacial radiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The head and neck and oral maxillofacial regions are composed of complex anatomical structures and contain various types of tissues. Radiological examination is one of the most important and widely used clinical approaches for understanding these regions. Advances in medical imaging acquisition and imaging analysis, including artificial intelligence, allow researchers and clinicians to understand the underlying physiology and pathology of these regions. These advances also offer the potential to integrate research findings to clinical practice.

Dr. Hemis Qiyong Ai
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-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

  • medical imaging
  • head and neck
  • machine learning
  • radiomics analysis
  • medical image analysis

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Published Papers (3 papers)

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Research

16 pages, 2118 KB  
Article
Poor Bone Health Associated with Reduced Cerebral Perfusion and Brain Volume in Older Adults
by Tiffany Y. So, James F. Griffith, Jill Abrigo, Lin Shi, David K. W. Yeung, Jason Leung, Timothy Kwok and Vincent C. T. Mok
Diagnostics 2026, 16(4), 529; https://doi.org/10.3390/diagnostics16040529 - 10 Feb 2026
Viewed by 487
Abstract
Background: Bone health and brain function may be closely interconnected through a complex bone–brain axis. The relationship between bone mineral density (BMD), vertebral perfusion, marrow composition, cerebral perfusion, brain volume, and cognitive decline, however, remain incompletely understood. Methods: Ninety-nine female subjects (mean age [...] Read more.
Background: Bone health and brain function may be closely interconnected through a complex bone–brain axis. The relationship between bone mineral density (BMD), vertebral perfusion, marrow composition, cerebral perfusion, brain volume, and cognitive decline, however, remain incompletely understood. Methods: Ninety-nine female subjects (mean age 65.00 ± 5.00 years) with clinically suspected mild cognitive impairment underwent dual-energy X-ray absorptiometry, carotid ultrasound, and multimodal magnetic resonance imaging (MRI) of the brain and lumbar spine to measure BMD, bone perfusion, marrow fat content as well as cerebral perfusion, cerebral volume, cerebral white matter burden and large vessel atherosclerosis. Cognitive function was assessed using the Hong Kong Montreal Cognitive Assessment (HK-MoCA). Bone, cerebral, vascular, and cognitive measures were correlated using Spearman correlation coefficients and compared in group comparisons. Results: Lower BMD was correlated with reduced subcortical cerebral blood flow (CBF) (r = 0.27, p = 0.031) and lower total brain parenchymal volume (r = 0.25, p = 0.021). Reduced bone marrow perfusion and increased marrow fat content were also associated with lower total brain parenchymal volume (r = 0.24, p = 0.023 and r = −0.26, p = 0.025). Subjects with the lowest L3 vertebral body perfusion or highest marrow fat content had significantly reduced total brain and hippocampal volumes (p = 0.029–0.049) compared with those with the highest perfusion or lowest marrow fat content. Conclusions: This study shows an association between lower BMD, reduced vertebral perfusion, and increased marrow fat with reduced brain parenchymal volumes and reduced brain perfusion. Further studies are warranted to clarify these relationships and explore the underlying shared mechanisms affecting bone health and cerebral microvascular and structural brain changes. Full article
(This article belongs to the Special Issue Advances in Head and Neck and Oral Maxillofacial Radiology)
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15 pages, 1916 KB  
Article
Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model
by Miriam Rinneburger, Heike Carolus, Andra-Iza Iuga, Mathilda Weisthoff, Simon Lennartz, Nils Große Hokamp, Liliana Lourenco Caldeira, Astha Jaiswal, David Maintz, Fabian Christopher Laqua, Bettina Baeßler, Tobias Klinder and Thorsten Persigehl
Diagnostics 2026, 16(2), 355; https://doi.org/10.3390/diagnostics16020355 - 21 Jan 2026
Viewed by 455
Abstract
Background/Objectives: Accurate assessment of lymph nodes is of paramount importance for correct cN staging in head and neck cancer; however, it is very time-consuming for radiologists, and lymph node metastases of head and neck cancers may show distinct characteristics, such as central necrosis [...] Read more.
Background/Objectives: Accurate assessment of lymph nodes is of paramount importance for correct cN staging in head and neck cancer; however, it is very time-consuming for radiologists, and lymph node metastases of head and neck cancers may show distinct characteristics, such as central necrosis or very large size. Here, we evaluate the performance of a previously developed generic cervical lymph node segmentation model in a cohort of patients with head and neck cancer. Methods: In our retrospective single-center, multi-vendor study, we included 125 patients with head and neck cancer with at least one untreated lymph node metastasis. On the respective cervical CT scan, an experienced radiologist segmented lymph nodes semi-automatically. All 3D segmentations were confirmed by a second reader. These manual segmentations were compared to segmentations generated by an AI model previously trained on a different dataset of varying cancers. Results: In cervical CT scans from 125 patients (61.9 years ± 10.6, 100 men), 3656 lymph nodes were segmented as ground-truth, including 544 clinical metastases. The AI achieved an average recall of 0.70 with 6.5 false positives per CT scan. The average global Dice accounts for 0.73 per scan, with an average Hausdorff distance of 0.88 mm. When analyzing the individual nodes, segmentation accuracy was similar for non-metastatic and metastatic lymph nodes, with a sensitivity of 0.89 and 0.85. Localization performance was lower for metastatic than for non-metastatic lymph nodes, with a recall of 0.65 and 0.74, respectively. Model performance was worse for enlarged nodes (short-axis diameter ≥ 15 mm), with a recall of 0.36 and a sensitivity of 0.67. Conclusions: The AI model for generic cervical lymph node segmentation shows good performance for smaller nodes (SAD ≤ 15 mm) with respect to localization and segmentation accuracy. However, for clearly enlarged and necrotic nodes, a retraining of the generic AI algorithm seems to be required for accurate cN staging. Full article
(This article belongs to the Special Issue Advances in Head and Neck and Oral Maxillofacial Radiology)
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14 pages, 851 KB  
Article
Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different Tooth Numbering Systems
by Zekai Liu, Qi Yong H. Ai, Andy Wai Kan Yeung, Ray Tanaka, Andrew Nalley and Kuo Feng Hung
Diagnostics 2025, 15(18), 2315; https://doi.org/10.3390/diagnostics15182315 - 12 Sep 2025
Cited by 2 | Viewed by 2133
Abstract
Objectives: The aim of this study was to evaluate the performance of GPT-4o in identifying nine common dental conditions on panoramic radiographs, both overall and at specific tooth sites, and to assess whether the use of different tooth numbering systems (FDI and [...] Read more.
Objectives: The aim of this study was to evaluate the performance of GPT-4o in identifying nine common dental conditions on panoramic radiographs, both overall and at specific tooth sites, and to assess whether the use of different tooth numbering systems (FDI and Universal) in prompts would affect its diagnostic accuracy. Methods: Fifty panoramic radiographs exhibiting various common dental conditions including missing teeth, impacted teeth, caries, endodontically treated teeth, teeth with restorations, periapical lesions, periodontal bone loss, tooth fractures, cracks, retained roots, dental implants, osteolytic lesions, and osteosclerosis were included. Each image was evaluated twice by GPT-4o in May 2025, using structured prompts based on either the FDI or Universal tooth numbering system, to identify the presence of these conditions at specific tooth sites or regions. GPT-4o responses were compared to a consensus reference standard established by an oral-maxillofacial radiology team. GPT-4o’s performance was evaluated using balanced accuracy, sensitivity, specificity, and F1 score both at the patient and tooth levels. Results: A total of 100 GPT-4o responses were generated. At the patient level, balanced accuracy ranged from 46.25% to 98.83% (FDI) and 49.75% to 92.86% (Universal), with the highest accuracies for dental implants (92.86–98.83%). F1-scores and sensitivities were highest for implants, missing, and impacted teeth, but zero for caries, periapical lesions, and fractures. Specificity was generally high across conditions. Notable discrepancies were observed between patient- and tooth-level performance, especially for implants and restorations. GPT-4o’s performance was similar between using the two numbering systems. Conclusions: GPT-4o demonstrated superior performance in detecting dental implants and treated or restored teeth but inferior performance for caries, periapical lesions, and fractures. Diagnostic accuracy was higher at the patient level than at the tooth level, with similar performances for both numbering systems. Future studies with larger, more diverse datasets and multiple models are needed. Full article
(This article belongs to the Special Issue Advances in Head and Neck and Oral Maxillofacial Radiology)
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