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Diagnostics
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The Advanced Role of Deep Learning and Radiomics in Maxillofacial...
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The Advanced Role of Deep Learning and Radiomics in Maxillofacial Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 9277

Special Issue Editors

Dr. Qi-Yong Hemis Ai

E-Mail Website
Guest Editor
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong
Interests: head and neck imaging; radiomics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Kuo Feng Hung

E-Mail Website
Guest Editor
Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, University of Hong Kong, Hong Kong
Interests: digital dentistry; dentomaxillofacial diagnostic imaging; image-guided oral surgery; artificial intelligence in oral medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Maxillofacial diseases cover both odontogenic and nonodontogenic diseases in the jaws and related structures, including the salivary glands, temporomandibular joints (TMJs), and facial muscles. Due to the anatomical complexity and close proximity to critical vascular and neural structures of these areas, medical imaging (such as CT, ultrasound, MRI, and nuclear imaging) serves as a crucial component for patient management, from diagnosis, treatment planning and monitoring to outcome prediction.

In the era of artificial intelligence, a wide range of deep learning and radiomics applications have been developed based on medical images for the clinical management of various maxillofacial diseases.

This Special Issue aims to collect original studies, literature reviews, and meta-analyses on the advanced role of deep learning and radiomics in maxillofacial imaging. Exploring the potential of these cutting-edge technologies could improve patient care in the investigated field.

Dr. Qi-Yong Hemis Ai
Dr. Kuo Feng Hung
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 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.

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Published Papers (4 papers)

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Research

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15 pages, 5003 KiB  
Article
Automatic Reproduction of Natural Head Position in Orthognathic Surgery Using a Geometric Deep Learning Network
by Ji-Yong Yoo, Su Yang, Sang-Heon Lim, Ji Yong Han, Jun-Min Kim, Jo-Eun Kim, Kyung-Hoe Huh, Sam-Sun Lee, Min-Suk Heo, Hoon Joo Yang and Won-Jin Yi
Diagnostics 2025, 15(1), 42; https://doi.org/10.3390/diagnostics15010042 - 27 Dec 2024
Viewed by 677
Abstract
Background: Accurate determination of the natural head position (NHP) is essential in orthognathic surgery for optimal surgical planning and improved patient outcomes. However, traditional methods encounter reproducibility issues and rely on external devices or patient cooperation, potentially leading to inaccuracies in the [...] Read more.
Background: Accurate determination of the natural head position (NHP) is essential in orthognathic surgery for optimal surgical planning and improved patient outcomes. However, traditional methods encounter reproducibility issues and rely on external devices or patient cooperation, potentially leading to inaccuracies in the surgical plan. Methods: To address these limitations, we developed a geometric deep learning network (NHP-Net) to automatically reproduce NHP from CT scans. A dataset of 150 orthognathic surgery patients was utilized. Three-dimensional skull meshes were converted into point clouds and normalized to fit within a unit sphere. NHP-Net was trained to predict a 3 × 3 rotation matrix to align the CT-acquired posture with the NHP. Experiments were conducted to determine optimal point cloud sizes and loss functions. Performance was evaluated using mean absolute error (MAE) for roll, pitch, and yaw angles, as well as a rotation error (RE) metric. Results: NHP-Net achieved the lowest RE of 1.918° ± 1.099° and demonstrated significantly lower MAEs in roll and pitch angles compared to other deep learning models (p < 0.05). These findings indicate that NHP-Net can accurately align CT-acquired postures to the NHP, enhancing the precision of surgical planning. Conclusions: By effectively improving the accuracy and efficiency of NHP reproduction, NHP-Net reduces the workload of surgeons, supports more precise orthognathic surgical interventions, and ultimately contributes to better patient outcomes. Full article
(This article belongs to the Special Issue The Advanced Role of Deep Learning and Radiomics in Maxillofacial Imaging)
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14 pages, 2929 KiB  
Article
Utilizing Deep Learning for Diagnosing Radicular Cysts
by Mario Rašić, Mario Tropčić, Jure Pupić-Bakrač, Marko Subašić, Igor Čvrljević and Emil Dediol
Diagnostics 2024, 14(13), 1443; https://doi.org/10.3390/diagnostics14131443 - 6 Jul 2024
Cited by 1 | Viewed by 1927
Abstract
Objectives: The purpose of this study was to develop a deep learning algorithm capable of diagnosing radicular cysts in the lower jaw on panoramic radiographs. Materials and Methods: In this study, we conducted a comprehensive analysis of 138 radicular cysts and 100 normal [...] Read more.
Objectives: The purpose of this study was to develop a deep learning algorithm capable of diagnosing radicular cysts in the lower jaw on panoramic radiographs. Materials and Methods: In this study, we conducted a comprehensive analysis of 138 radicular cysts and 100 normal panoramic radiographs collected from 2013 to 2023 at Clinical Hospital Dubrava. The images were annotated by a team comprising a radiologist and a maxillofacial surgeon, utilizing the GNU Image Manipulation Program. Furthermore, the dataset was enriched through the application of various augmentation techniques to improve its robustness. The evaluation of the algorithm’s performance and a deep dive into its mechanics were achieved using performance metrics and EigenCAM maps. Results: In the task of diagnosing radicular cysts, the initial algorithm performance—without the use of augmentation techniques—yielded the following scores: precision at 85.8%, recall at 66.7%, mean average precision (mAP)@50 threshold at 70.9%, and mAP@50-95 thresholds at 60.2%. The introduction of image augmentation techniques led to the precision of 74%, recall of 77.8%, mAP@50 threshold to 89.6%, and mAP@50-95 thresholds of 71.7, respectively. Also, the precision and recall were transformed into F1 scores to provide a balanced evaluation of model performance. The weighted function of these metrics determined the overall efficacy of our models. In our evaluation, non-augmented data achieved F1 scores of 0.750, while augmented data achieved slightly higher scores of 0.758. Conclusion: Our study underscores the pivotal role that deep learning is poised to play in the future of oral and maxillofacial radiology. Furthermore, the algorithm developed through this research demonstrates a capability to diagnose radicular cysts accurately, heralding a significant advancement in the field. Full article
(This article belongs to the Special Issue The Advanced Role of Deep Learning and Radiomics in Maxillofacial Imaging)
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10 pages, 881 KiB  
Article
Complex Craniofacial Cases through Augmented Reality Guidance in Surgical Oncology: A Technical Report
by Alessandro Tel, Luca Raccampo, Shankeeth Vinayahalingam, Stefania Troise, Vincenzo Abbate, Giovanni Dell’Aversana Orabona, Salvatore Sembronio and Massimo Robiony
Diagnostics 2024, 14(11), 1108; https://doi.org/10.3390/diagnostics14111108 - 27 May 2024
Cited by 1 | Viewed by 1135
Abstract
Augmented reality (AR) is a promising technology to enhance image guided surgery and represents the perfect bridge to combine precise virtual planning with computer-aided execution of surgical maneuvers in the operating room. In craniofacial surgical oncology, AR brings to the surgeon’s sight a [...] Read more.
Augmented reality (AR) is a promising technology to enhance image guided surgery and represents the perfect bridge to combine precise virtual planning with computer-aided execution of surgical maneuvers in the operating room. In craniofacial surgical oncology, AR brings to the surgeon’s sight a digital, three-dimensional representation of the anatomy and helps to identify tumor boundaries and optimal surgical paths. Intraoperatively, real-time AR guidance provides surgeons with accurate spatial information, ensuring accurate tumor resection and preservation of critical structures. In this paper, the authors review current evidence of AR applications in craniofacial surgery, focusing on real surgical applications, and compare existing literature with their experience during an AR and navigation guided craniofacial resection, to subsequently analyze which technological trajectories will represent the future of AR and define new perspectives of application for this revolutionizing technology. Full article
(This article belongs to the Special Issue The Advanced Role of Deep Learning and Radiomics in Maxillofacial Imaging)
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Review

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28 pages, 1657 KiB  
Review
Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion
by Zhuoying Li, Kuo Feng Hung, Qi Yong H. Ai, Min Gu, Yu-xiong Su and Zhiyi Shan
Diagnostics 2024, 14(5), 544; https://doi.org/10.3390/diagnostics14050544 - 4 Mar 2024
Cited by 5 | Viewed by 4609
Abstract
Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific [...] Read more.
Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients’ facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Full article
(This article belongs to the Special Issue The Advanced Role of Deep Learning and Radiomics in Maxillofacial Imaging)
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