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Shujaat S, Alfadley A, Morgan N, Jamleh A, Riaz M, Aboalela AA, Jacobs R. Emergence of artificial intelligence for automating cone-beam computed tomography-derived maxillary sinus imaging tasks. A systematic review. Clin Implant Dent Relat Res 2024. [PMID: 38863306 DOI: 10.1111/cid.13352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/16/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024]
Abstract
Cone-beam computed tomography (CBCT) imaging of the maxillary sinus is indispensable for implantologists, offering three-dimensional anatomical visualization, morphological variation detection, and abnormality identification, all critical for diagnostics and treatment planning in digital implant workflows. The following systematic review presented the current evidence pertaining to the use of artificial intelligence (AI) for CBCT-derived maxillary sinus imaging tasks. An electronic search was conducted on PubMed, Web of Science, and Cochrane up until January 2024. Based on the eligibility criteria, 14 articles were included that reported on the use of AI for the automation of CBCT-derived maxillary sinus assessment tasks. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool was used to evaluate the risk of bias and applicability concerns. The AI models used were designed to automate tasks such as segmentation, classification, and prediction. Most studies related to automated maxillary sinus segmentation demonstrated high performance. In terms of classification tasks, the highest accuracy was observed for diagnosing sinusitis (99.7%), whereas the lowest accuracy was detected for classifying abnormalities such as fungal balls and chronic rhinosinusitis (83.0%). Regarding implant treatment planning, the classification of automated surgical plans for maxillary sinus floor augmentation based on residual bone height showed high accuracy (97%). Additionally, AI demonstrated high performance in predicting gender and sinus volume. In conclusion, although AI shows promising potential in automating maxillary sinus imaging tasks which could be useful for diagnostic and planning tasks in implantology, there is a need for more diverse datasets to improve the generalizability and clinical relevance of AI models. Future studies are suggested to focus on expanding the datasets, making the AI model's source available, and adhering to standardized AI reporting guidelines.
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Affiliation(s)
- Sohaib Shujaat
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Abdulmohsen Alfadley
- King Abdullah International Medical Research Center, Department of Restorative and Prosthetic Dental Sciences, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Nermin Morgan
- Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Ahmed Jamleh
- Department of Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, UAE
| | - Marryam Riaz
- Department of Physiology, Azra Naheed Dental College, Superior University, Lahore, Pakistan
| | - Ali Anwar Aboalela
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Section of Oral Diagnostics and Surgery, Department of Dental Medicine, Division of Oral Diagnostics and Rehabilitation, Karolinska Institutet, Huddinge, Sweden
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Jacobs R, Fontenele RC, Lahoud P, Shujaat S, Bornstein MM. Radiographic diagnosis of periodontal diseases - Current evidence versus innovations. Periodontol 2000 2024. [PMID: 38831570 DOI: 10.1111/prd.12580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/23/2024] [Accepted: 05/16/2024] [Indexed: 06/05/2024]
Abstract
Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.
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Affiliation(s)
- Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Rocharles Cavalcante Fontenele
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Pierre Lahoud
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Leuven, Belgium
| | - Sohaib Shujaat
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Michael M Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
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Alfadley A, Shujaat S, Jamleh A, Riaz M, Aboalela AA, Ma H, Orhan K. Progress of artificial intelligence-driven solutions for automated segmentation of dental pulp cavity on cone-beam computed tomography images. A systematic review. J Endod 2024:S0099-2399(24)00336-4. [PMID: 38821262 DOI: 10.1016/j.joen.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/02/2024]
Abstract
INTRODUCTION Automated segmentation of three-dimensional pulp space on cone-beam computed tomography (CBCT) images presents a significant opportunity for enhancing diagnosis, treatment planning, and clinical education in endodontics. The aim of this systematic review was to investigate the performance of AI-driven automated pulp space segmentation on CBCT images. METHODS A comprehensive electronic search was performed using PubMed, Web of Science, and Cochrane databases, up until February 2024. Two independent reviewers participated in the selection of studies, data extraction, and evaluation of the included studies. Any disagreements were resolved by a third reviewer. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias. RESULTS Thirteen studies that met the eligibility criteria were included. Most studies demonstrated high accuracy in their respective segmentation methods, although there was some variation across different structures (pulp chamber, root canal) and tooth types (single-rooted, multi-rooted). Automated segmentation showed slightly superior performance for segmenting the pulp chamber compared to the root canal and single-rooted teeth compared to multi-rooted ones. Furthermore, second mesiobuccal (MB2) canal segmentation also demonstrated high performance. In terms of time efficiency, the minimum time required for segmentation was 13 seconds. CONCLUSION AI-driven models demonstrated outstanding performance in pulp space segmentation. Nevertheless, these findings warrant careful interpretation, and their generalizability is limited due to the potential risk and low evidence level arising from inadequately detailed methodologies and inconsistent assessment techniques. In addition, there is room for further improvement, specifically for root canal segmentation and testing of AI performance in artifact-induced images.
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Affiliation(s)
- Abdulmohsen Alfadley
- King Abdullah International Medical Research Center, Department of Restorative and Prosthetic Dental Sciences, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia.
| | - Sohaib Shujaat
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia; OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Ahmed Jamleh
- Department of Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Marryam Riaz
- Department of Physiology, Azra Naheed Dental College, Superior University, Lahore, Pakistan
| | - Ali Anwar Aboalela
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Hongyang Ma
- 2nd dental center, School of Stomatology, Peking University
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Elsonbaty S, Elgarba BM, Fontenele RC, Swaity A, Jacobs R. Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study. Int J Paediatr Dent 2024. [PMID: 38769619 DOI: 10.1111/ipd.13204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/25/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. AIM The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). DESIGN A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. RESULTS AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). CONCLUSION The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.
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Affiliation(s)
- Sara Elsonbaty
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Egyptian Ministry of Health and Population, Cairo, Egypt
| | - Bahaaeldeen M Elgarba
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Prosthodontics, Faculty of Dentistry, Tanta University, Tanta, Egypt
| | - Rocharles Cavalcante Fontenele
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Abdullah Swaity
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
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Elgarba BM, Fontenele RC, Tarce M, Jacobs R. Artificial intelligence serving pre-surgical digital implant planning: A scoping review. J Dent 2024; 143:104862. [PMID: 38336018 DOI: 10.1016/j.jdent.2024.104862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
OBJECTIVES To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software. DATA AND SOURCES A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed. STUDY SELECTION AND RESULTS From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol. CONCLUSIONS AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated. CLINICAL SIGNIFICANCE Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.
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Affiliation(s)
- Bahaaeldeen M Elgarba
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Prosthodontics, Faculty of Dentistry, Tanta University, 31511 Tanta, Egypt.
| | - Rocharles Cavalcante Fontenele
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium
| | - Mihai Tarce
- Division of Periodontology & Implant Dentistry, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China & Periodontology and Oral Microbiology, Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
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Nogueira-Reis F, Morgan N, Suryani IR, Tabchoury CPM, Jacobs R. Full virtual patient generated by artificial intelligence-driven integrated segmentation of craniomaxillofacial structures from CBCT images. J Dent 2024; 141:104829. [PMID: 38163456 DOI: 10.1016/j.jdent.2023.104829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/13/2023] [Accepted: 12/29/2023] [Indexed: 01/03/2024] Open
Abstract
OBJECTIVES To assess the performance, time-efficiency, and consistency of a convolutional neural network (CNN) based automated approach for integrated segmentation of craniomaxillofacial structures compared with semi-automated method for creating a virtual patient using cone beam computed tomography (CBCT) scans. METHODS Thirty CBCT scans were selected. Six craniomaxillofacial structures, encompassing the maxillofacial complex bones, maxillary sinus, dentition, mandible, mandibular canal, and pharyngeal airway space, were segmented on these scans using semi-automated and composite of previously validated CNN-based automated segmentation techniques for individual structures. A qualitative assessment of the automated segmentation revealed the need for minor refinements, which were manually corrected. These refined segmentations served as a reference for comparing semi-automated and automated integrated segmentations. RESULTS The majority of minor adjustments with the automated approach involved under-segmentation of sinus mucosal thickening and regions with reduced bone thickness within the maxillofacial complex. The automated and the semi-automated approaches required an average time of 1.1 min and 48.4 min, respectively. The automated method demonstrated a greater degree of similarity (99.6 %) to the reference than the semi-automated approach (88.3 %). The standard deviation values for all metrics with the automated approach were low, indicating a high consistency. CONCLUSIONS The CNN-driven integrated segmentation approach proved to be accurate, time-efficient, and consistent for creating a CBCT-derived virtual patient through simultaneous segmentation of craniomaxillofacial structures. CLINICAL RELEVANCE The creation of a virtual orofacial patient using an automated approach could potentially transform personalized digital workflows. This advancement could be particularly beneficial for treatment planning in a variety of dental and maxillofacial specialties.
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Affiliation(s)
- Fernanda Nogueira-Reis
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo 13414‑903, Brazil
| | - Nermin Morgan
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Dakahlia 35516, Egypt
| | - Isti Rahayu Suryani
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Cinthia Pereira Machado Tabchoury
- Department of Biosciences, Division of Biochemistry, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo 13414‑903, Brazil
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven Kapucijnenvoer 7, Leuven 3000, Belgium; Department of Dental Medicine, Karolinska Institutet, Box 4064, Huddinge, Stockholm 141 04, Sweden.
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Swaity A, Elgarba BM, Morgan N, Ali S, Shujaat S, Borsci E, Chilvarquer I, Jacobs R. Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images. Sci Rep 2024; 14:369. [PMID: 38172136 PMCID: PMC10764895 DOI: 10.1038/s41598-023-49613-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/10/2023] [Indexed: 01/05/2024] Open
Abstract
The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image. A total of 100 CBCT images with maxillary canine impactions were randomly allocated into two groups: a training set (n = 50) and a testing set (n = 50). The training set was used to train the CNN model and the testing set was employed to evaluate the model performance. Both tasks were performed on an online cloud-based platform, 'Virtual patient creator' (Relu, Leuven, Belgium). The performance was assessed using voxel- and surface-based comparison between automated and semi-automated ground truth segmentations. In addition, the time required for segmentation was also calculated. The automated tool showed high performance for segmenting impacted canines with a dice similarity coefficient of 0.99 ± 0.02. Moreover, it was 24 times faster than semi-automated approach. The proposed CNN model achieved fast, consistent, and precise segmentation of maxillary impacted canines.
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Affiliation(s)
- Abdullah Swaity
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Prosthodontic Department, King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Bahaaeldeen M Elgarba
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Prosthodontics, Tanta University, Tanta, Egypt
| | - Nermin Morgan
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Saleem Ali
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Restorative Dentistry Department, King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Elena Borsci
- Oral Diagnostic Clinic, Karolinska Institute, Stockholm, Sweden
| | - Israel Chilvarquer
- Department of Oral Radiology, School of Dentistry, University of São Paulo (USP), São Paulo, Brazil
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.
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Shujaat S, Politis C, Van Den Bogaert T, Vueghs P, Smeets M, Verhelst PJ, Grymonprez E, Jacobs R. Morphological characteristics of coronoid process and revisiting definition of coronoid hyperplasia. Sci Rep 2023; 13:21049. [PMID: 38030618 PMCID: PMC10687078 DOI: 10.1038/s41598-023-46289-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
The aim of this study was to assess the morphological characteristics of the coronoid process (CP) and define coronoid hyperplasia (CH) using cadaveric mandibles of a Caucasian population. A sample of 151 adult dry cadaveric mandibles (302 CPs) was acquired. Three distances were measured, which included the width, height, and length of CP. The surface area measurements involved area A: above the width distance line; area B: between incisura mandibulae-Alveolar ridge line and width distance line; area C: between distance lines of width and height. Finally, angulations of the CP and gonial angles were identified. Both length and surface area A + B acted as hyperplastic indicators. Based on the selection criteria, a sample of 197 CPs was included. The hooked shape (59%) was most commonly observed. No significant difference existed between left and right sides (p > 0.05). The mean values of length and surface area A + B were 2.2 ± 0.3 cm and 3.3 ± 0.8 cm2, and any values above 2.7 cm (n = 5 CPs- 2.5%) and 5.0 cm2 (n = 9 CPs- 4.6%) were described as hyperplastic, respectively. The presented data could act as quantitative reference for differentiating between normal and hyperplastic conditions.
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Affiliation(s)
- Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia.
| | - Constantinus Politis
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Tom Van Den Bogaert
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Pieter Vueghs
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Maximiliaan Smeets
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Pieter-Jan Verhelst
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Edouard Grymonprez
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
- Section of Oral Diagnostics and Surgery, Department of Dental Medicine, Division of Oral Diagnostics and Rehabilitation, Karolinska Institutet, Huddinge, Sweden.
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Zhang L, Li W, Lv J, Xu J, Zhou H, Li G, Ai K. Advancements in oral and maxillofacial surgery medical images segmentation techniques: An overview. J Dent 2023; 138:104727. [PMID: 37769934 DOI: 10.1016/j.jdent.2023.104727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023] Open
Abstract
OBJECTIVES This article reviews recent advances in computer-aided segmentation methods for oral and maxillofacial surgery and describes the advantages and limitations of these methods. The objective is to provide an invaluable resource for precise therapy and surgical planning in oral and maxillofacial surgery. Study selection, data and sources: This review includes full-text articles and conference proceedings reporting the application of segmentation methods in the field of oral and maxillofacial surgery. The research focuses on three aspects: tooth detection segmentation, mandibular canal segmentation and alveolar bone segmentation. The most commonly used imaging technique is CBCT, followed by conventional CT and Orthopantomography. A systematic electronic database search was performed up to July 2023 (Medline via PubMed, IEEE Xplore, ArXiv, Google Scholar were searched). RESULTS These segmentation methods can be mainly divided into two categories: traditional image processing and machine learning (including deep learning). Performance testing on a dataset of images labeled by medical professionals shows that it performs similarly to dentists' annotations, confirming its effectiveness. However, no studies have evaluated its practical application value. CONCLUSION Segmentation methods (particularly deep learning methods) have demonstrated unprecedented performance, while inherent challenges remain, including the scarcity and inconsistency of datasets, visible artifacts in images, unbalanced data distribution, and the "black box" nature. CLINICAL SIGNIFICANCE Accurate image segmentation is critical for precise treatment and surgical planning in oral and maxillofacial surgery. This review aims to facilitate more accurate and effective surgical treatment planning among dental researchers.
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Affiliation(s)
- Lang Zhang
- School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China
| | - Wang Li
- School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China.
| | - Jinxun Lv
- School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China
| | - Jiajie Xu
- School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China
| | - Hengyu Zhou
- School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China
| | - Gen Li
- School of Biomedical Engineering, Chongqing University of Technology, Chongqing 400054, China
| | - Keqi Ai
- Department of Radiology, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
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Elgarba BM, Van Aelst S, Swaity A, Morgan N, Shujaat S, Jacobs R. Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study. J Dent 2023; 137:104639. [PMID: 37517787 DOI: 10.1016/j.jdent.2023.104639] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/01/2023] Open
Abstract
OBJECTIVES To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images. METHODS A total dataset of 280 maxillomandibular jawbone CBCT scans was acquired from patients who underwent implant placement with or without coronal restoration. The dataset was randomly divided into three subsets: training set (n = 225), validation set (n = 25) and testing set (n = 30). A CNN model was developed and trained using expert-based semi-automated segmentation (SS) of the implant and attached prosthetic crown as the ground truth. The performance of AS was assessed by comparing with SS and manually corrected automated segmentation referred to as refined-automated segmentation (R-AS). Evaluation metrics included timing, voxel-wise comparison based on confusion matrix and 3D surface differences. RESULTS The average time required for AS was 60 times faster (<30 s) than the SS approach. The CNN model was highly effective in segmenting dental implants both with and without coronal restoration, achieving a high dice similarity coefficient score of 0.92±0.02 and 0.91±0.03, respectively. Moreover, the root mean square deviation values were also found to be low (implant only: 0.08±0.09 mm, implant+restoration: 0.11±0.07 mm) when compared with R-AS, implying high AI segmentation accuracy. CONCLUSIONS The proposed cloud-based deep learning tool demonstrated high performance and time-efficient segmentation of implants on CBCT images. CLINICAL SIGNIFICANCE AI-based segmentation of implants and prosthetic crowns can minimize the negative impact of artifacts and enhance the generalizability of creating dental virtual models. Furthermore, incorporating the suggested tool into existing CNN models specialized for segmenting anatomical structures can improve pre-surgical planning for implants and post-operative assessment of peri‑implant bone levels.
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Affiliation(s)
- Bahaaeldeen M Elgarba
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Department of Prosthodontics, Faculty of Dentistry, Tanta University, 31511 Tanta, Egypt
| | - Stijn Van Aelst
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium
| | - Abdullah Swaity
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Prosthodontic Department, King Hussein Medical Center, Royal Medical Services, Amman, Jordan
| | - Nermin Morgan
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Sohaib Shujaat
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.
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Morgan N, Meeus J, Shujaat S, Cortellini S, Bornstein MM, Jacobs R. CBCT for Diagnostics, Treatment Planning and Monitoring of Sinus Floor Elevation Procedures. Diagnostics (Basel) 2023; 13:diagnostics13101684. [PMID: 37238169 DOI: 10.3390/diagnostics13101684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/05/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Sinus floor elevation (SFE) is a standard surgical technique used to compensate for alveolar bone resorption in the posterior maxilla. Such a surgical procedure requires radiographic imaging pre- and postoperatively for diagnosis, treatment planning, and outcome assessment. Cone beam computed tomography (CBCT) has become a well-established imaging modality in the dentomaxillofacial region. The following narrative review is aimed to provide clinicians with an overview of the role of three-dimensional (3D) CBCT imaging for diagnostics, treatment planning, and postoperative monitoring of SFE procedures. CBCT imaging prior to SFE provides surgeons with a more detailed view of the surgical site, allows for the detection of potential pathologies three-dimensionally, and helps to virtually plan the procedure more precisely while reducing patient morbidity. In addition, it serves as a useful follow-up tool for assessing sinus and bone graft changes. Meanwhile, using CBCT imaging has to be standardized and justified based on the recognized diagnostic imaging guidelines, taking into account both the technical and clinical considerations. Future studies are recommended to incorporate artificial intelligence-based solutions for automating and standardizing the diagnostic and decision-making process in the context of SFE procedures to further improve the standards of patient care.
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Affiliation(s)
- Nermin Morgan
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura 35516, Egypt
| | - Jan Meeus
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Campus Sint-Rafael, 3000 Leuven, Belgium
| | - Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Campus Sint-Rafael, 3000 Leuven, Belgium
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 11426, Saudi Arabia
| | - Simone Cortellini
- Department of Oral Health Sciences, Section of Periodontology, KU Leuven, 3000 Leuven, Belgium
- Department of Dentistry, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Michael M Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, 4058 Basel, Switzerland
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Campus Sint-Rafael, 3000 Leuven, Belgium
- Department of Dental Medicine, Karolinska Institute, 141 04 Huddinge, Sweden
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Nogueira-Reis F, Morgan N, Nomidis S, Van Gerven A, Oliveira-Santos N, Jacobs R, Tabchoury CPM. Three-dimensional maxillary virtual patient creation by convolutional neural network-based segmentation on cone-beam computed tomography images. Clin Oral Investig 2023; 27:1133-1141. [PMID: 36114907 PMCID: PMC9985582 DOI: 10.1007/s00784-022-04708-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/01/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To qualitatively and quantitatively assess integrated segmentation of three convolutional neural network (CNN) models for the creation of a maxillary virtual patient (MVP) from cone-beam computed tomography (CBCT) images. MATERIALS AND METHODS A dataset of 40 CBCT scans acquired with different scanning parameters was selected. Three previously validated individual CNN models were integrated to achieve a combined segmentation of maxillary complex, maxillary sinuses, and upper dentition. Two experts performed a qualitative assessment, scoring-integrated segmentations from 0 to 10 based on the number of required refinements. Furthermore, experts executed refinements, allowing performance comparison between integrated automated segmentation (AS) and refined segmentation (RS) models. Inter-observer consistency of the refinements and the time needed to create a full-resolution automatic segmentation were calculated. RESULTS From the dataset, 85% scored 7-10, and 15% were within 3-6. The average time required for automated segmentation was 1.7 min. Performance metrics indicated an excellent overlap between automatic and refined segmentation with a dice similarity coefficient (DSC) of 99.3%. High inter-observer consistency of refinements was observed, with a 95% Hausdorff distance (HD) of 0.045 mm. CONCLUSION The integrated CNN models proved to be fast, accurate, and consistent along with a strong interobserver consistency in creating the MVP. CLINICAL RELEVANCE The automated segmentation of these structures simultaneously could act as a valuable tool in clinical orthodontics, implant rehabilitation, and any oral or maxillofacial surgical procedures, where visualization of MVP and its relationship with surrounding structures is a necessity for reaching an accurate diagnosis and patient-specific treatment planning.
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Affiliation(s)
- Fernanda Nogueira-Reis
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo, 13414‑903, Brazil.,OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium
| | - Nermin Morgan
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium.,Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura , 35516, Dakahlia, Egypt
| | | | | | - Nicolly Oliveira-Santos
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo, 13414‑903, Brazil.,OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium. .,Department of Dental Medicine, Karolinska Institutet, Box 4064, 141 04, Huddinge, Stockholm, Sweden.
| | - Cinthia Pereira Machado Tabchoury
- Department of Biosciences, Division of Biochemistry, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo, 13414‑903, Brazil
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Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning. Clin Oral Investig 2023; 27:897-906. [PMID: 36323803 DOI: 10.1007/s00784-022-04706-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/29/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The aim of this review was to investigate the application of artificial intelligence (AI) in maxillofacial computer-assisted surgical planning (CASP) workflows with the discussion of limitations and possible future directions. MATERIALS AND METHODS An in-depth search of the literature was undertaken to review articles concerned with the application of AI for segmentation, multimodal image registration, virtual surgical planning (VSP), and three-dimensional (3D) printing steps of the maxillofacial CASP workflows. RESULTS The existing AI models were trained to address individual steps of CASP, and no single intelligent workflow was found encompassing all steps of the planning process. Segmentation of dentomaxillofacial tissue from computed tomography (CT)/cone-beam CT imaging was the most commonly explored area which could be applicable in a clinical setting. Nevertheless, a lack of generalizability was the main issue, as the majority of models were trained with the data derived from a single device and imaging protocol which might not offer similar performance when considering other devices. In relation to registration, VSP and 3D printing, the presence of inadequate heterogeneous data limits the automatization of these tasks. CONCLUSION The synergy between AI and CASP workflows has the potential to improve the planning precision and efficacy. However, there is a need for future studies with big data before the emergent technology finds application in a real clinical setting. CLINICAL RELEVANCE The implementation of AI models in maxillofacial CASP workflows could minimize a surgeon's workload and increase efficiency and consistency of the planning process, meanwhile enhancing the patient-specific predictability.
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14
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Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol 2023; 52:20220335. [PMID: 36472627 PMCID: PMC9793453 DOI: 10.1259/dmfr.20220335] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.
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Affiliation(s)
- Kuo Feng Hung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Michael M. Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
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15
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:diagnostics13010110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Correspondence:
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Thurzo A, Strunga M, Havlínová R, Reháková K, Urban R, Surovková J, Kurilová V. Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics? SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207752. [PMID: 36298103 PMCID: PMC9607180 DOI: 10.3390/s22207752] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/05/2022] [Accepted: 10/11/2022] [Indexed: 05/28/2023]
Abstract
The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones' TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions with amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.
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Affiliation(s)
- Andrej Thurzo
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Martin Strunga
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Romana Havlínová
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Katarína Reháková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Renata Urban
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Jana Surovková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Veronika Kurilová
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovičova 3, 81219 Bratislava, Slovakia
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Ayidh Alqahtani K, Jacobs R, Smolders A, Van Gerven A, Willems H, Shujaat S, Shaheen E. Deep convolutional neural network-based automated segmentation and classification of teeth with orthodontic brackets on cone-beam computed-tomographic images: a validation study. Eur J Orthod 2022; 45:169-174. [PMID: 36099419 DOI: 10.1093/ejo/cjac047] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Tooth segmentation and classification from cone-beam computed tomography (CBCT) is a prerequisite for diagnosis and treatment planning in the majority of digital dental workflows. However, an accurate and efficient segmentation of teeth in the presence of metal artefacts still remains a challenge. Therefore, the following study aimed to validate an automated deep convolutional neural network (CNN)-based tool for the segmentation and classification of teeth with orthodontic brackets on CBCT images. METHODS A total of 215 CBCT scans (1780 teeth) were retrospectively collected, consisting of pre- and post-operative images of the patients who underwent combined orthodontic and orthognathic surgical treatment. All the scans were acquired with NewTom CBCT device. A complete dentition with orthodontic brackets and high-quality images were included. The dataset were randomly divided into three subsets with random allocation of all 32 tooth classes: training set (140 CBCT scans-400 teeth), validation set (35 CBCT scans-100 teeth), and test set (pre-operative: 25, post-operative: 15 = 40 CBCT scans-1280 teeth). A multiclass CNN-based tool was developed and its performance was assessed for automated segmentation and classification of teeth with brackets by comparison with a ground truth. RESULTS The CNN model took 13.7 ± 1.2 s for the segmentation and classification of all the teeth on a single CBCT image. Overall, the segmentation performance was excellent with a high intersection over union (IoU) of 0.99. Anterior teeth showed a significantly lower IoU (P < 0.05) compared to premolar and molar teeth. The dice similarity coefficient score of anterior (0.99 ± 0.02) and premolar teeth (0.99 ± 0.10) in the pre-operative group was comparable to the post-operative group. The classification of teeth to the correct 32 classes had a high recall rate (99.9%) and precision (99%). CONCLUSIONS The proposed CNN model outperformed other state-of-the-art algorithms in terms of accuracy and efficiency. It could act as a viable alternative for automatic segmentation and classification of teeth with brackets. CLINICAL SIGNIFICANCE The proposed method could simplify the existing digital workflows of orthodontics, orthognathic surgery, restorative dentistry, and dental implantology by offering an accurate and efficient automated segmentation approach to clinicians, hence further enhancing the treatment predictability and outcomes.
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Affiliation(s)
- Khalid Ayidh Alqahtani
- Department of Imaging and Pathology, Faculty of Medicine, KU Leuven and Department of Oral and Maxillofacial Surgery, OMFS IMPATH Research Group, University Hospitals Leuven, Leuven, Belgium.,Department of Oral and Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
| | - Reinhilde Jacobs
- Department of Imaging and Pathology, Faculty of Medicine, KU Leuven and Department of Oral and Maxillofacial Surgery, OMFS IMPATH Research Group, University Hospitals Leuven, Leuven, Belgium.,Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | - Sohaib Shujaat
- Department of Imaging and Pathology, Faculty of Medicine, KU Leuven and Department of Oral and Maxillofacial Surgery, OMFS IMPATH Research Group, University Hospitals Leuven, Leuven, Belgium
| | - Eman Shaheen
- Department of Imaging and Pathology, Faculty of Medicine, KU Leuven and Department of Oral and Maxillofacial Surgery, OMFS IMPATH Research Group, University Hospitals Leuven, Leuven, Belgium
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Preda F, Morgan N, Van Gerven A, Nogueira-Reis F, Smolders A, Wang X, Nomidis S, Shaheen E, Willems H, Jacobs R. Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography - A validation study. J Dent 2022; 124:104238. [PMID: 35872223 DOI: 10.1016/j.jdent.2022.104238] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 07/14/2022] [Accepted: 07/17/2022] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES The present study investigated the accuracy, consistency, and time-efficiency of a novel deep CNN-based model for the automated maxillofacial bone segmentation from CBCT images. METHOD A dataset of 144 scans was acquired from two CBCT devices and randomly divided into three subsets: training set (n= 110), validation set (n= 10) and testing set (n=24). A three-dimensional (3D) U-Net (CNN) model was developed, and the achieved automated segmentation was compared with a manual approach. RESULTS The average time required for automated segmentation was 39.1 seconds with a 204-fold decrease in time consumption compared to manual segmentation (132.7 minutes). The model is highly accurate for identification of the bony structures of the anatomical region of interest with a dice similarity coefficient (DSC) of 92.6%. Additionally, the fully deterministic nature of the CNN model was able to provide 100% consistency without any variability. The inter-observer consistency for expert-based minor correction of the automated segmentation observed an excellent DSC of 99.7%. CONCLUSION The proposed CNN model provided a time-efficient, accurate, and consistent CBCT-based automated segmentation of the maxillofacial complex. CLINICAL SIGNIFICANCE Automated segmentation of the maxillofacial complex could act as a potent alternative to the conventional segmentation techniques for improving the efficiency of the digital workflows. This approach could deliver an accurate and ready-to-print three dimensional (3D) models that are essential to patient-specific digital treatment planning for orthodontics, maxillofacial surgery, and implant placement.
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Affiliation(s)
- Flavia Preda
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer33, BE-3000 Leuven, Belgium.
| | - Nermin Morgan
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer33, BE-3000 Leuven, Belgium; Department of Oral Medicine, Faculty of Dentistry, Mansoura University, 35516 Mansoura, Dakahlia, Egypt
| | | | - Fernanda Nogueira-Reis
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer33, BE-3000 Leuven, Belgium; Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Av. Limeira 901, Piracicaba, São Paulo 13414‑903, Brazil
| | | | - Xiaotong Wang
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer33, BE-3000 Leuven, Belgium
| | | | - Eman Shaheen
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer33, BE-3000 Leuven, Belgium
| | | | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer33, BE-3000 Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Box 4064, 141 04 Huddinge, Stockholm, Sweden
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Jha S, Balachandran R, Sharma S, Kumar V, Chawla A, Logani A. A novel approach to repositioning and stabilization of a luxated tooth with displacement using a 3D printed guide. J Endod 2022; 48:936-942. [DOI: 10.1016/j.joen.2022.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/28/2022] [Accepted: 03/31/2022] [Indexed: 11/30/2022]
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3D Printing of Tooth Impressions Based on Multi-Detector Computed Tomography Images Combined with Beam Hardening Artifact Reduction in Metal Structures. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
We investigated the role of metal artifact reduction by taking 3D print impressions using 3D data of Computed Tomography (CT) images based on the algorithm applied. We manufactured a phantom of a human mandible tooth made of gypsum and nickel alloy to measure the metal artifacts. CT images were obtained by changing the phantom tube voltage and tube current. The signal intensity of the image generated by the metal artifacts before and after the iterative metal artifact reduction algorithm (iMAR) was measured. A 3D printing process was performed after converting the images, before and after iMAR application, into STL files using InVesalius version 3.1.1 by selecting the conditions that minimized the effect of the artifact. Regarding metal artifacts, the Hounsfield unit (HU) value showed low as the tube voltage increased. The iMAR-applied images acquired under the same conditions showed a significantly lower HU. The artifacts, in the form of flashes, persisted in the 3D-printed product of the image not subjected to iMAR, but were largely removed in the 3D-printed product following iMAR application. In this study, the application of iMAR and data acquired using high tube voltage eliminated a significant portion of the metal artifacts, resulting in an impression shape that was consistent with the human body.
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Kuralt M, Kučič AC, Gašperšič R, Fidler A. Evaluation of gingival recessions with conventional versus digital methods. J Dent 2022; 120:104093. [PMID: 35301080 DOI: 10.1016/j.jdent.2022.104093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/10/2022] [Accepted: 03/13/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES The present study aimed to compare the conventional clinical and digital methods evaluating differences in gingival recession (ΔREC) in patients with advanced periodontitis treated with the non-surgical treatment protocol. METHODS Agreement between the methods was evaluated on a sample of ten patients with periodontitis (stage III/IV, grade B/C) with acquired clinical measurements and digital models from baseline (T0) and 12-months after non-surgical treatment of periodontitis (T1). The evaluation was performed on maxillary teeth from right to left second premolar resulting in overall 99 teeth. Clinical evaluation was performed by subtracting the distance measurements between gingival margin and cemento-enamel junction, obtained at T0 and T1 by a calibrated examiner (intra-examiner agreement >90%). The digital evaluation was performed directly by measuring the distance between the gingival margins on superimposed T0 and T1 digital models. Using Bland-Altman and statistical analysis, all six measurements sites around each included tooth (n=594) acquired with both methods were compared. RESULTS Median ΔREC (5th and 95th percentile) acquired with a conventional clinical and digital method was 0.0mm (-2.0 - 1.0) and -0.4mm (-1.6 - 0.8), respectively (p<0.0001). The complete agreement between rounded digital and clinical ΔREC values was only 38%, revealing high disagreement also confirmed by Bland-Altman analysis with 95% limits of agreement ranging from -2.6 to 1.8mm. Absolute differences between the methods higher than 0.5 and 1 mm, was found in 61% and 38% of measurement sites, respectively. CONCLUSIONS The conventional clinical method for ΔREC evaluation exhibits lower sensitivity and accuracy than the digital method. CLINICAL SIGNIFICANCE The quality of both clinical and research data in periodontology and implantology can be considerably improved by the digital method while still preserving the compatibility with the conventional clinical method.
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Affiliation(s)
- Marko Kuralt
- Department of Restorative Dentistry and Endodontics, University Medical Centre Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Slovenia.
| | | | - Rok Gašperšič
- Department of Oral Medicine and Periodontology, University Medical Centre Ljubljana, Slovenia; Department of Oral Medicine and Periodontology, Faculty of Medicine, University of Ljubljana, Slovenia
| | - Aleš Fidler
- Department of Restorative Dentistry and Endodontics, University Medical Centre Ljubljana, Slovenia; Department of Endodontics and Operative Dentistry, Faculty of Medicine, University of Ljubljana, Slovenia
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Fontenele RC, Gerhardt MDN, Pinto JC, Van Gerven A, Willems H, Jacobs R, Freitas DQ. Influence of dental fillings and tooth type on performance of a novel artificial intelligence-driven tool for automatic tooth segmentation on CBCT images – A validation study. J Dent 2022; 119:104069. [DOI: 10.1016/j.jdent.2022.104069] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/26/2022] [Accepted: 02/16/2022] [Indexed: 01/11/2023] Open
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Barenghi L, Barenghi A, Garagiola U, Di Blasio A, Giannì AB, Spadari F. Pros and Cons of CAD/CAM Technology for Infection Prevention in Dental Settings during COVID-19 Outbreak. SENSORS (BASEL, SWITZERLAND) 2021; 22:49. [PMID: 35009586 PMCID: PMC8747329 DOI: 10.3390/s22010049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
The purpose of this commentary is to update the evidence reported in our previous review on the advantages and limitations of computer-aided design/computer-aided manufacturing technology in the promotion of dental business, as well as to guarantee patient and occupational safety. The COVID-19 pandemic led to an unprecedented focus on infection prevention; however, waves of COVID-19 follow one another, asymptomatic cases are nearly impossible to identify by triage in a dental setting, and the effectiveness of long-lasting immune protection through vaccination remains largely unknown. Different national laws and international guidelines (mainly USA-CDC, ECDC) have often brought about dissimilar awareness and operational choices, and in general, there has been very limited attention to this technology. Here, we discuss its advantages and limitations in light of: (a) presence of SARS-CoV-2 in the oral cavity, saliva, and dental biofilm and activation of dormant microbial infections; (b) the prevention of SARS-CoV-2 transmission by aerosol and fomite contamination; (c) the detection of various oral manifestations of COVID-19; (d) specific information for the reprocessing of the scanner tip and the ward from the manufacturers.
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Affiliation(s)
- Livia Barenghi
- Department of Biomedical, Surgical and Dental Sciences, University of Milano, 20122 Milan, Italy; (U.G.); (A.B.G.); (F.S.)
| | - Alberto Barenghi
- Department of Medicine and Surgery, Centro di Odontoiatria, Parma University, 43126 Parma, Italy; (A.B.); (A.D.B.)
| | - Umberto Garagiola
- Department of Biomedical, Surgical and Dental Sciences, University of Milano, 20122 Milan, Italy; (U.G.); (A.B.G.); (F.S.)
| | - Alberto Di Blasio
- Department of Medicine and Surgery, Centro di Odontoiatria, Parma University, 43126 Parma, Italy; (A.B.); (A.D.B.)
| | - Aldo Bruno Giannì
- Department of Biomedical, Surgical and Dental Sciences, University of Milano, 20122 Milan, Italy; (U.G.); (A.B.G.); (F.S.)
| | - Francesco Spadari
- Department of Biomedical, Surgical and Dental Sciences, University of Milano, 20122 Milan, Italy; (U.G.); (A.B.G.); (F.S.)
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