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Bioengineering
Special Issues
AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields
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AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1431

Special Issue Editors

Dr. Antonino Lo Giudice

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Guest Editor
Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, 95131 Catania, Italy
Interests: 3D imaging; CBCT; digital anatomical segmentation; facial scan; intraoral scan; cephalometry; craniofacial development imaging; CAD-CAM; diagnostic digital workflow; RMI; functional orthodontic appliances; dentofacial orthopedics; interceptive orthodontics; elastodontics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rosalia Leonardi

E-Mail Website
Guest Editor
Unit of Orthodontics, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, University of Catania, 95131 Catania, Italy
Interests: 3D imaging; CBCT; artificial intelligence (AI); digital anatomical segmentation; facial scan; intra-oral scan; cephalometry; craniofacial development imaging; CAD-CAM; diagnostic digital workflow; RMI; functional orthodontic appliances; dentofacial orthopedics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionizing the way diseases are diagnosed and treated. In the dentistry and orofacial fields, AI offers immense potential for enhancing precision, efficiency, and personalization in diagnosis and treatment planning. This Special Issue aims to explore the latest advancements, challenges, and future directions in leveraging AI for diagnosis and treatment plans in the context of dentistry and orofacial disciplines.

This Special Issue will encompass a broad range of topics related to AI applications in dentistry and orofacial fields, including but not limited to the following:

AI-powered diagnostic tools: Development and validation of AI algorithms for the accurate and early diagnosis of dental and orofacial conditions.

Machine learning in treatment planning: Utilizing machine learning models to optimize and personalize treatment plans based on patient-specific data.

Integration of AI in imaging analysis: Enhancing diagnostic imaging interpretation through AI-based analysis in radiology and 3D imaging in dentistry.

Predictive modeling for oral diseases: AI-driven approaches to predict the onset and progression of oral diseases, enabling proactive and preventive interventions.

Virtual and augmented reality in treatment simulation: Applications of AI-enhanced virtual and augmented reality for simulating treatment outcomes and patient education.

Ethical considerations and challenges: Exploration of ethical implications, patient privacy, and regulatory considerations associated with the integration of AI in dentistry and orofacial treatment planning.

We invite researchers, practitioners, and experts in the field to submit original research articles, reviews, and case studies that contribute to the understanding and advancement of AI in the dentistry and orofacial fields. Submissions should adhere to the journal's guidelines and will undergo a rigorous peer review process.

Prof. Dr. Antonino Lo Giudice
Prof. Dr. Rosalia Leonardi
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • artificial intelligence
  • AI
  • digital dentistry
  • digital systems
  • CBCT
  • CAD-CAM

Published Papers (2 papers)

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Research

17 pages, 4127 KiB  
Article
Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions
by Zhiyang Liu, Dong Yang, Minghao Zhang, Guohua Liu, Qian Zhang and Xiaonan Li
Bioengineering 2024, 11(4), 354; https://doi.org/10.3390/bioengineering11040354 - 05 Apr 2024
Viewed by 522
Abstract
Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam [...] Read more.
Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55% and 81.35% in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39% and 2.82% gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts. Full article
(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)
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10 pages, 12696 KiB  
Article
A Comparative Analysis of Artificial Intelligence and Manual Methods for Three-Dimensional Anatomical Landmark Identification in Dentofacial Treatment Planning
by Hee-Ju Ahn, Soo-Hwan Byun, Sae-Hoon Baek, Sang-Yoon Park, Sang-Min Yi, In-Young Park, Sung-Woon On, Jong-Cheol Kim and Byoung-Eun Yang
Bioengineering 2024, 11(4), 318; https://doi.org/10.3390/bioengineering11040318 - 27 Mar 2024
Viewed by 646
Abstract
With the growing demand for orthognathic surgery and other facial treatments, the accurate identification of anatomical landmarks has become crucial. Recent advancements have shifted towards using three-dimensional radiologic analysis instead of traditional two-dimensional methods, as it allows for more precise treatment planning, primarily [...] Read more.
With the growing demand for orthognathic surgery and other facial treatments, the accurate identification of anatomical landmarks has become crucial. Recent advancements have shifted towards using three-dimensional radiologic analysis instead of traditional two-dimensional methods, as it allows for more precise treatment planning, primarily relying on direct identification by clinicians. However, manual tracing can be time-consuming, mainly when dealing with a large number of patients. This study compared the accuracy and reliability of identifying anatomical landmarks using artificial intelligence (AI) and manual identification. Thirty patients over 19 years old who underwent pre-orthodontic and orthognathic surgery treatment and had pre-orthodontic three-dimensional radiologic scans were selected. Thirteen anatomical indicators were identified using both AI and manual methods. The landmarks were identified by AI and four experienced clinicians, and multiple ANOVA was performed to analyze the results. The study results revealed minimal significant differences between AI and manual tracing, with a maximum deviation of less than 2.83 mm. This indicates that utilizing AI to identify anatomical landmarks can be a reliable method in planning orthognathic surgery. Our findings suggest that using AI for anatomical landmark identification can enhance treatment accuracy and reliability, ultimately benefiting clinicians and patients. Full article
(This article belongs to the Special Issue AI-Powered Diagnosis and Treatment Plans in Dentistry and Orofacial Fields)
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