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Diagnostics
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Advancements in Craniofacial Practices: Imaging, AI, Surgery, and Patient Care
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Advancements in Craniofacial Practices: Imaging, AI, Surgery, and Patient Care

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2597

Special Issue Editor

Dr. Rami R. Hallac

E-Mail Website
Guest Editor
1. Analytical Imaging and Modeling Center, Children’s Health, Dallas, TX, 75235, USA
2. Department of Plastic Surgery, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Interests: machine learning; craniofacial imaging; ear deformity; craniosynostosis; cleft lip and palate; vascular anomalies; 3D photogrammetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the evolving landscape of craniofacial healthcare, exploring innovations across imaging, artificial intelligence, surgical techniques, and patient-centered care. The aim is to provide a comprehensive overview of the practices that are driving advances in diagnosis, treatment, and holistic patient management. Key themes include the following:

  1. Imaging Innovations: Advances in craniofacial imaging technologies, such as radiological imaging, 3D photogrammetry, 4D stereophotogrammetry, CBCT, and multimodal approaches, as well as their integration into diagnostic and surgical workflows.
  2. Artificial Intelligence and Machine Learning: The role of AI in automating craniofacial image analysis, improving diagnostic accuracy, predicting outcomes, and supporting clinical decision-making.
  3. Surgical Advances: Cutting-edge surgical techniques, including minimally invasive methods, 3D-printed implants, personalized surgical planning, and the use of robotics in craniofacial reconstruction.
  4. Patient-Centered Care: Approaches that emphasize comprehensive patient management, including interdisciplinary collaboration between surgeons, orthodontists, and allied healthcare professionals, as well as considerations for quality of life, functional outcomes, and psychological support.
  5. Precision Medicine and Personalized Treatment: How modern craniofacial practices are leveraging precision medicine to develop individualized treatment plans that address the unique anatomical, genetic, and clinical characteristics of patients.
  6. Telehealth and Remote Monitoring: The growing role of telehealth in craniofacial care, facilitating remote consultations and follow-up care and expanding access to specialized services in underserved populations.
  7. Future Challenges and Opportunities: Identifying ongoing challenges in craniofacial healthcare, including the incorporation of new technologies, regulatory considerations, addressing healthcare disparities, and future directions for research and clinical practice.

Dr. Rami R. Hallac
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • craniofacial imaging
  • 3D radiological imaging
  • 3D photogrammetry
  • 4D stereophotogrammetry
  • artificial intelligence (AI)
  • craniofacial surgery
  • patient-centered care
  • 3D printing
  • precision medicine
  • telehealth
  • surgical planning
  • interdisciplinary collaboration

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Published Papers (4 papers)

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14 pages, 5627 KB  
Article
U-Net-Based Deep Learning for Simultaneous Segmentation and Agenesis Detection of Primary and Permanent Teeth in Panoramic Radiographs
by Hamit Tunç, Nurullah Akkaya, Berkehan Aykanat and Gürkan Ünsal
Diagnostics 2025, 15(20), 2577; https://doi.org/10.3390/diagnostics15202577 - 13 Oct 2025
Viewed by 444
Abstract
Background/Objectives: Panoramic radiographs aid diagnosis in paediatric dentistry, but errors occur. Deep learning-based artificial intelligence offers improved accuracy by reducing overlap-related and interpretive mistakes. This study aimed to develop a U-Net-based deep learning model for simultaneous tooth segmentation and agenesis detection, capable [...] Read more.
Background/Objectives: Panoramic radiographs aid diagnosis in paediatric dentistry, but errors occur. Deep learning-based artificial intelligence offers improved accuracy by reducing overlap-related and interpretive mistakes. This study aimed to develop a U-Net-based deep learning model for simultaneous tooth segmentation and agenesis detection, capable of distinguishing between primary and permanent teeth in panoramic radiographs. Methods: Publicly available panoramic radiographs, along with images collected from the archives of Burdur Mehmet Akif Ersoy University Faculty of Dentistry, were used. The dataset totalled 1697 panoramic radiographs after applying exclusion criteria for artifacts and edentulous cases. Manual segmentation was performed by two paediatric dentists and one dentomaxillofacial radiologist. The images were split into training (80%), validation (10%), and test (10%) sets. A U-Net architecture was trained to identify both primary and permanent teeth and to detect tooth agenesis. Results: Dental agenesis was detected in 14.6% of 1697 OPGs, predominantly affecting the mandibular second premolars (32.5%) and maxillary lateral incisors (27.6%). Intra- and inter-researcher intraclass correlation coefficients (ICCs) were 0.995 and 0.990, respectively (p > 0.05). On the test set, the model achieved a Dice similarity coefficient of 0.8773, precision of 0.9115, recall of 0.8974, and an F1 score of 0.9027. Validation accuracy was 96.71%, indicating reliable performance across diverse datasets. Conclusions: The proposed deep learning model automates tooth segmentation and agenesis detection for both primary and permanent dentitions in panoramic radiographs. Its high-performance metrics suggest improved accuracy and efficiency in paediatric dental diagnostics, potentially reducing clinician workload and minimizing diagnostic errors. Full article
(This article belongs to the Special Issue Advancements in Craniofacial Practices: Imaging, AI, Surgery, and Patient Care)
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13 pages, 1244 KB  
Article
Sella Turcica and Cranial Base Symmetry in Anterior Synostotic Plagiocephaly Patients: A Retrospective Case–Control Study
by Edoardo Staderini, Davide Guerrieri, Michele Tepedino, Gianmarco Saponaro, Alessandro Moro, Giulio Gasparini, Patrizia Gallenzi and Massimo Cordaro
Diagnostics 2025, 15(17), 2199; https://doi.org/10.3390/diagnostics15172199 - 29 Aug 2025
Viewed by 651
Abstract
Background/Objectives: The present case–control study aims to compare the symmetry of the sella turcica and cranial base of nine patients with anterior unicoronal synostotic plagiocephaly (ASP) and nine healthy patients referred to the maxillofacial unit of the Fondazione Policlinico Universitario Agostino Gemelli. [...] Read more.
Background/Objectives: The present case–control study aims to compare the symmetry of the sella turcica and cranial base of nine patients with anterior unicoronal synostotic plagiocephaly (ASP) and nine healthy patients referred to the maxillofacial unit of the Fondazione Policlinico Universitario Agostino Gemelli. The primary aim of this study is to assess changes in the morphology of the sella turcica and skull base in comparison with a healthy control population using both a 2D and 3D analysis of the sella turcica and skull base. Methods: Computed tomography (CT) scans of nine ASP patients from the Fondazione Policlinico Universitario Agostino Gemelli in Rome were retrieved. A quantitative evaluation of the skull base and the sella turcica was performed through the asymmetry index (A.I.), obtained from the comparison of the point-to-point distances ipsilateral and contralateral to the synostosis. A qualitative three-dimensional (3D) evaluation of the asymmetry of the sella turcica was performed by comparing each sella model with its mirrored counterpart; then, the root mean square (RMS) displacement between the original and mirrored 3D models was calculated. Results: The results showed higher A.I. values in the study group, particularly the length of the anterior cranial fossa, with A.I. values of 7.96 (study) vs. 0.02 (control). Conclusions: The higher values of the asymmetry index observed in the study group supported the presence of statistically significant asymmetries in the sella and cranial fossa measurements compared to the control group. Full article
(This article belongs to the Special Issue Advancements in Craniofacial Practices: Imaging, AI, Surgery, and Patient Care)
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12 pages, 796 KB  
Article
Enhancing Predictive Tools for Skeletal Growth and Craniofacial Morphology in Syndromic Craniosynostosis: A Focus on Cranial Base Variables
by Lantian Zheng, Norli Anida Abdullah, Norlisah Mohd Ramli, Nur Anisah Mohamed, Mohamad Norikmal Fazli Hisam and Firdaus Hariri
Diagnostics 2025, 15(13), 1640; https://doi.org/10.3390/diagnostics15131640 - 27 Jun 2025
Viewed by 719
Abstract
Background/Objectives: Patients with syndromic craniosynostosis (SC) pose a significant challenge for post-operational outcomes due to the variability in craniofacial deformities and gain-of-function characteristics. This study aims to develop validated predictive tools using stable cranial base variables to predict changes in the midfacial [...] Read more.
Background/Objectives: Patients with syndromic craniosynostosis (SC) pose a significant challenge for post-operational outcomes due to the variability in craniofacial deformities and gain-of-function characteristics. This study aims to develop validated predictive tools using stable cranial base variables to predict changes in the midfacial region and explore the craniofacial morphology among patients with SC. Methods: This study involved 17 SC patients under 12 years old, 17 age-matched controls for morphological analysis, and 21 normal children for developing craniofacial predictive models. A stable cranial base and changeable midfacial variables were analyzed using the Mann–Whitney U test. Pearson correlation identified linear relationships between the midface and cranial base variables. Multicollinearity was checked before fitting the data with multiple linear regression for growth prediction. Model adequacy was confirmed and the 3-fold cross-validation ensured results reliability. Results: Patients with SC exhibited a shortened cranial base, particularly in the middle cranial fossa (S-SO), and a sharper N-S-SO and N-SO-BA angle, indicating a downward rotation and kyphosis. The midface length (ANS-PNS) and zygomatic length (ZMs-ZTi) were significantly reduced, while the midface width (ZFL-ZFR) was increased. Regression models for the midface length, width, and zygomatic length were given as follows: ANS-PNS = 23.976 + 0.139 S-N + 0.545 SO-BA − 0.120 N-S-BA + 0.078 S-SO-BA + 0.051 age (R2 = 0.978, RMSE = 1.058); ZFL-ZFR = −15.618 + 0.666 S-N + 0.241 N-S-BA + 0.155 S-SO-BA + 0.121 age (R2 = 0.903, RMSE = 3.158); and ZMs-ZTi = −14.403 + 0.765 SO-BA + 0.266 N-S-BA + 0.111 age (R2 = 0.878, RMSE = 3.720), respectively. Conclusions: The proposed models have potential applications for midfacial growth estimation in children with SC. Full article
(This article belongs to the Special Issue Advancements in Craniofacial Practices: Imaging, AI, Surgery, and Patient Care)
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23 pages, 1347 KB  
Systematic Review
Imaging Modalities in Craniosynostosis: A Systematic Review and Proposal of the ARCANA Protocol for Multimodal Radiation-Free Assessment
by Mirko Micovic, Bojana Zivkovic, Ivan Vukasinovic, Drago Jelovac, Milan Stojicic and Vladimir Bascarevic
Diagnostics 2025, 15(20), 2632; https://doi.org/10.3390/diagnostics15202632 - 18 Oct 2025
Viewed by 322
Abstract
Background/Objective: Craniosynostosis, the premature fusion of one or more cranial sutures, is the second most common craniofacial defect and poses significant diagnostic and therapeutic challenges. Our objective was to systematically evaluate current diagnostic imaging modalities for craniosynostosis and to propose a novel radiation-free [...] Read more.
Background/Objective: Craniosynostosis, the premature fusion of one or more cranial sutures, is the second most common craniofacial defect and poses significant diagnostic and therapeutic challenges. Our objective was to systematically evaluate current diagnostic imaging modalities for craniosynostosis and to propose a novel radiation-free ARCANA Protocol as an alternative to conventional screening. Methods: Following PRISMA guidelines, we conducted a systematic review of the literature using PubMed and Cochrane databases from 2015 onwards, restricted to English-language and full-text articles. Inclusion criteria encompassed studies evaluating diagnostic accuracy, radiation exposure, and neurocranial outcomes associated with imaging modalities in craniosynostosis. Quality assessment was performed using QUADAS-2. To evaluate the certainty of evidence supporting each imaging modality, we applied the GRADE framework. Given the extensive number of included studies (n = 70), findings were categorized by diagnostic modality rather than individual studies. Results: Analysis of 70 selected studies demonstrated a continued reliance on 3D computed tomography (3DCT) as the diagnostic gold standard, despite recognized risks of cumulative radiation exposure in pediatric populations. Alternative radiation-free imaging techniques including high-resolution ultrasonography (US), three-dimensional stereophotogrammetry (3DSPG), and advanced magnetic resonance imaging (MRI) have emerged, offering substantial benefits such as eliminating ionizing radiation and providing comprehensive neurocranial assessments. 3DCT demonstrates approximately 90% sensitivity and 90–100% specificity for detecting suture closure; ultrasound achieves 71–100% sensitivity and 86–100% specificity, while advanced MRI techniques such as GA-VIBE report up to 97% sensitivity and 96% specificity. Conclusions: The proposed ARCANA Protocol integrates clinical assessment, 3DSPG, US, and advanced MRI sequences into a unified multimodal framework that eliminates radiation exposure while ensuring comprehensive evaluation of cranial and intracranial anatomy. The protocol emphasizes patient safety and diagnostic accuracy. The main limitations of this study are the heterogeneity of the included studies and the lack of prospective validation, which is essential to confirm diagnostic and clinical effectiveness and to support a potential paradigm shift toward radiation-free assessment of craniosynostosis. Full article
(This article belongs to the Special Issue Advancements in Craniofacial Practices: Imaging, AI, Surgery, and Patient Care)
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