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Polizzi A, Leonardi R. Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews. J Dent 2024; 146:105056. [PMID: 38729291 DOI: 10.1016/j.jdent.2024.105056] [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: 03/11/2024] [Revised: 04/25/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVES The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) performance in automatic 2D and 3D cephalometric landmark identification. DATA A combination of free text words and MeSH keywords pooled by boolean operators: Automa* AND cephalo* AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "learning"). SOURCES A search strategy without a timeframe setting was conducted on PubMed, Scopus, Web of Science, Cochrane Library and LILACS. STUDY SELECTION The study protocol followed the PRISMA guidelines and the PICO question was formulated according to the aim of the article. The database search led to the selection of 15 articles that were assessed for eligibility in full-text. Finally, 11 systematic reviews met the inclusion criteria and were analyzed according to the risk of bias in systematic reviews (ROBIS) tool. CONCLUSIONS AI was not able to identify the various cephalometric landmarks with the same accuracy. Since most of the included studies' conclusions were based on a wrong 2 mm cut-off difference between the AI automatic landmark location and that allocated by human operators, future research should focus on refining the most powerful architectures to improve the clinical relevance of AI-driven automatic cephalometric analysis. CLINICAL SIGNIFICANCE Despite a progressively improved performance, AI has exceeded the recommended magnitude of error for most cephalometric landmarks. Moreover, AI automatic landmarking on 3D CBCT appeared to be less accurate compared to that on 2D X-rays. To date, AI-driven cephalometric landmarking still requires the final supervision of an experienced orthodontist.
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Affiliation(s)
- Alessandro Polizzi
- Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy.
| | - Rosalia Leonardi
- Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy
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Rodrigues J, Evangelopoulos E, Anagnostopoulos I, Sachdev N, Ismail A, Samsudin R, Khalaf K, Pattanaik S, Shetty SR. Impact of class II and class III skeletal malocclusion on pharyngeal airway dimensions: A systematic literature review and meta-analysis. Heliyon 2024; 10:e27284. [PMID: 38501020 PMCID: PMC10945137 DOI: 10.1016/j.heliyon.2024.e27284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 02/24/2024] [Accepted: 02/27/2024] [Indexed: 03/20/2024] Open
Abstract
Background This study is a pioneer systematic review and meta-analysis aimed at comparing the influence of Class II and Class III skeletal malocclusions on pharyngeal airway dimensions. It stands as the inaugural comprehensive assessment to collate and analyze the disparate findings from previously published articles on this topic. The objective of this study was to identify published articles that compare the effects of class II and class III skeletal malocclusion on the pharyngeal airway dimensions. Methods An all-inclusive search for existing published studies was done to identify peer-reviewed scholarly articles that compared the influence of class II and class III skeletal malocclusion on pharyngeal airway dimensions. The search was done via five electronic databases: Cochrane Library, EMBASE, Scopus, Web of Science, and PubMed. Screening of the articles was done and the eligible studies were critically assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. Results The initial search yielded 476 potential articles of which, nine were finally included in this study for a total of 866 patients. Three studies were of cross-sectional design and six were of retrospective study design. Following a critical analysis and review of the studies, class III skeletal malocclusion had significantly larger volume and area measurements compared to class II skeletal malocclusion. Conclusion Research in the field of literature has established that variations in skeletal classifications have a discernible effect on the size of the pharyngeal airways. With the advancement of skeletal malocclusions to a class III, there is an observed increase in both the volume and cross-sectional area of the airways.
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Affiliation(s)
- Jensyll Rodrigues
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | | | | | | | - Ahmad Ismail
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Rani Samsudin
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Khaled Khalaf
- Institute of Dentistry, University of Aberdeen, United Kingdom
| | - Snigdha Pattanaik
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Shishir Ram Shetty
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
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Alam MK, Alftaikhah SAA, Issrani R, Ronsivalle V, Lo Giudice A, Cicciù M, Minervini G. Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies. Heliyon 2024; 10:e24221. [PMID: 38317889 PMCID: PMC10838702 DOI: 10.1016/j.heliyon.2024.e24221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Background In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies. Methods A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings. Results 9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications. Conclusion In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.
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Affiliation(s)
- Mohammad Khursheed Alam
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
- Department of Dental Research Cell, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Chennai, 600077, India
- Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh
| | | | - Rakhi Issrani
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
| | - Vincenzo Ronsivalle
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Antonino Lo Giudice
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Marco Cicciù
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Giuseppe Minervini
- Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania “Luigi Vanvitelli”, 80121, Naples, Italy
- Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Science (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
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Minervini G, Franco R, Marrapodi MM, Lo Giudice A, Cicciù M, Ronsivalle V. Dental implant survival in epidermolysis bullosa patients: A systematic review conducted according to PRISMA guidelines and the Cochrane handbook for systematic reviews of interventions. Heliyon 2024; 10:e24208. [PMID: 38304847 PMCID: PMC10831621 DOI: 10.1016/j.heliyon.2024.e24208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/23/2023] [Accepted: 01/04/2024] [Indexed: 02/03/2024] Open
Abstract
Background Epidermolysis bullosa (EB) is a genetic syndrome afflicting skin and mucous membranes. The manifestation depends on the form: in mild conditions, occasionally, vesicular-bullous lesions of the oral cavity may be present, which heal spontaneously without leaving scars. Patients following joint rupture have scars that limit food intake and restrict quality of life. This study aims to evaluate the possibility of carrying out an implant therapy and the success rate of this therapy. Methods Until January 3, 2000, PubMed, Web of Science, and Lilacs were searched. Clinical studies were selected that considered implant therapy in patients with epidermolysis bullosa. Articles were therefore selected that addressed oral health and implant survival in patients with epidermolysis, with no differentiation between the various subtypes. A risk of bias assessment was performed through Cochrane. Results Twenty-one studies were found after the investigation. Only five were chosen to create the current systematic study; 16 articles were skipped over. 10 papers were disregarded because they had been reviewed; 4 were ignored because they contained case studies; and two were omitted because they were not written in English. The results show that implant survival is at around 97%. Conclusions Patients with epidermolysis bullosa can be treated with implant therapy without the risk of an increased implant failure rate. Indicate the main conclusions or interpretations.
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Affiliation(s)
- Giuseppe Minervini
- Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Rocco Franco
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Maria Maddalena Marrapodi
- Department of Woman, Child and General and Specialist Surgery, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Antonino Lo Giudice
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, CT, Italy
| | - Marco Cicciù
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, CT, Italy
| | - Vincenzo Ronsivalle
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, CT, Italy
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Iruvuri AG, Miryala G, Khan Y, Ramalingam NT, Sevugaperumal B, Soman M, Padmanabhan A. Revolutionizing Dental Imaging: A Comprehensive Study on the Integration of Artificial Intelligence in Dental and Maxillofacial Radiology. Cureus 2023; 15:e50292. [PMID: 38205468 PMCID: PMC10776831 DOI: 10.7759/cureus.50292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/08/2023] [Indexed: 01/12/2024] Open
Abstract
Recent advancements in deep learning and artificial intelligence (AI) have profoundly impacted various fields, including diagnostic imaging. Integrating AI technologies such as deep learning and convolutional neural networks has the potential to drastically improve diagnostic methods in the field of dentistry and maxillofacial radiography. A systematic study that adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards was carried out to examine the efficacy and uses of AI in dentistry and maxillofacial radiography. Incorporating cohort studies, case-control studies, and randomized clinical trials, the study used an interdisciplinary methodology. A thorough search spanning peer-reviewed research papers from 2009 to 2023 was done in databases including MEDLINE/PubMed and EMBASE. The inclusion criteria were original clinical research in English that employed AI models to recognize anatomical components in oral and maxillofacial pictures, identify anomalies, and diagnose disorders. The study looked at numerous research that used cutting-edge technology to show how accurate and dependable dental imaging is. Among the tasks covered by these investigations were age estimation, periapical lesion detection, segmentation of maxillary structures, assessment of dentofacial abnormalities, and segmentation of the mandibular canal. The study revealed important developments in the precise definition of anatomical structures and the identification of diseases. The use of AI technology in dental imaging marks a revolutionary development that will usher in a time of unmatched accuracy and effectiveness. These technologies have not only improved diagnostic accuracy and enabled early disease detection but have also streamlined intricate procedures, significantly enhancing patient outcomes. The symbiotic collaboration between human expertise and machine intelligence promises a future of more sophisticated and empathetic oral healthcare.
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Affiliation(s)
- Alekhya G Iruvuri
- General Dentistry, Malla Reddy Dental College for Women, Hyderabad, IND
| | - Gouthami Miryala
- General Dentistry, SVS Institute of Dental Sciences, Mahabubnagar, IND
| | - Yusuf Khan
- Orthodontics and Dentofacial Orthopaedics, Diamond Medical Specialists, Taif, SAU
| | | | - Bharath Sevugaperumal
- General Dentistry, Rajah Muthiah Dental College and Hospital, Annamalai University, Chidambaram, IND
| | - Mrunmayee Soman
- Dentistry, Dr. D. Y. Patil Dental College and Hospital, Pune, IND
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Ronsivalle V, Nucci L, Bua N, Palazzo G, La Rosa S. Elastodontic Appliances for the Interception of Malocclusion in Children: A Systematic Narrative Hybrid Review. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1821. [PMID: 38002912 PMCID: PMC10670240 DOI: 10.3390/children10111821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Interceptive orthodontic treatment aims to eliminate factors that prevent the harmonious development of the maxillary and mandibular arches during childhood, and elastodontic appliances (EAs) represent a group of devices with an increasingly important role. This systematic narrative hybrid review (HR) aims to provide an overview of the clinical indications for the use of EAs according to the available evidence and to identify potential research areas for unexplored applications. MATERIALS AND METHODS To assess the available literature on the subject, selective database searches were performed between July 2023 and September 2023. With the assistance of a health sciences librarian, a search strategy that utilized terms related to elastodontic therapy was developed. Embase, Scopus, PubMed, and Web of Science were the databases used. RESULTS The current literature addressing the usability of EAs is scarce and mostly limited to case reports and case series. After 2168 citations were found through the searches, 13 studies were ultimately included. In this regard, information about the clinical use and effectiveness of EAs are reported in a narrative form, defining specific domains of the application that are clinically oriented, including sagittal and transversal discrepancies, atypical swallowing, teeth malposition, two-phase orthodontics and a lack of teeth retention. CONCLUSIONS Within the intrinsic quality limitation of the available literature, it seems that EAs may represent a promising treatment alternative for managing mild-to-moderate malocclusion in children as an adjuvant therapy to the interruption of spoiled habits.
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Affiliation(s)
- Vincenzo Ronsivalle
- Department of Medical-Surgical Specialties—Section of Orthodontics, School of Dentistry, University of Catania, Policlinico Universitario “G. Rodolico-San Marco”, 95123 Catania, Italy; (N.B.); (G.P.); (S.L.R.)
| | - Ludovica Nucci
- Department of Medical-Surgical and Dental Specialties, University of Campania Luigi Vanvitelli, 80100 Naples, Italy
| | - Nicolò Bua
- Department of Medical-Surgical Specialties—Section of Orthodontics, School of Dentistry, University of Catania, Policlinico Universitario “G. Rodolico-San Marco”, 95123 Catania, Italy; (N.B.); (G.P.); (S.L.R.)
| | - Giuseppe Palazzo
- Department of Medical-Surgical Specialties—Section of Orthodontics, School of Dentistry, University of Catania, Policlinico Universitario “G. Rodolico-San Marco”, 95123 Catania, Italy; (N.B.); (G.P.); (S.L.R.)
| | - Salvatore La Rosa
- Department of Medical-Surgical Specialties—Section of Orthodontics, School of Dentistry, University of Catania, Policlinico Universitario “G. Rodolico-San Marco”, 95123 Catania, Italy; (N.B.); (G.P.); (S.L.R.)
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Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel) 2023; 11:2760. [PMID: 37893833 PMCID: PMC10606213 DOI: 10.3390/healthcare11202760] [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: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.
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Affiliation(s)
- Junqi Liu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Chengfei Zhang
- Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
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Kucukkaraca E. Is There a Relationship Between Unilateral/Bilateral Impacted Maxillary Canines and Nasal Septum Deviation? Cureus 2023; 15:e47931. [PMID: 38034237 PMCID: PMC10684973 DOI: 10.7759/cureus.47931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/29/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction A deviated nasal septum may be associated with some dentofacial deformities. The aim of the study was to determine whether there is a relationship between some craniomaxillary features of unilateral and bilateral maxillary impacted canines and nasal septum deviation. Methods This is a retrospective study consisting of cone beam computed tomography (CBCT) images of 51 patients. All patients were divided into three subgroups: unilateral maxillary impacted canines (UMIC) (n=19) bilateral maxillary impacted canines (BMIC) (n=15), and control group (MC) (n=17). The septal deviation angle and some angular and dimensional measurements were performed. Differences in linear and angular measurements between the groups were analyzed using One-way ANOVA and the Kruskal-Wallis test. Pearson's correlation analysis was performed to determine the relationship between the septal deviation angle, septal deviation direction, nasal floor angle, and other parameters, and multivariate linear regression analysis was performed to determine the effect of variables in the septal deviation angle. Results Bilateral or unilateral position of the impacted canines was found to be effective on septal deviation. The septal deviation angle and the nasal floor angle values were found to be significantly higher in the UMIC and BMIC groups (p<0.001) than in the MC group. Maxillary width was found to be significantly lower in the BMIC group compared to the UMIC (p<0.01) and MC group (p<0.001). Septal deviation angle was positively correlated with septal deviation direction and nasal floor angle (p<0.001). Palatal width and nasal floor angle were found to be negatively correlated (p<0.05), and palatal depth and septal deviation direction were found to be positively correlated (p<0.01). Groups and septal deviation angle, septal deviation direction, and nasal floor angle were found to be negatively correlated (p<0.001). The multivariate linear regression analysis revealed an association between septal deviation angle, group (p<0.01), and nasal floor angle (p<0.05). Conclusion Bilateral or unilateral position of the impacted canines was found to be effective on septal deviation. The septal deviation angle values were found to be higher when the maxillary impacted canine was unilateral. Unilateral or bilateral positions of the impacted canine and the nasal floor angle were found to be factors affecting the formation of septal deviation.
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Affiliation(s)
- Ebru Kucukkaraca
- Department of Orthodontics, Faculty of Dentistry, Ankara Yildirim Beyazit University, Ankara, TUR
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Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
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Jin S, Han H, Huang Z, Xiang Y, Du M, Hua F, Guan X, Liu J, Chen F, He H. Automatic three-dimensional nasal and pharyngeal airway subregions identification via Vision Transformer. J Dent 2023; 136:104595. [PMID: 37343616 DOI: 10.1016/j.jdent.2023.104595] [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: 04/06/2023] [Revised: 06/06/2023] [Accepted: 06/19/2023] [Indexed: 06/23/2023] Open
Abstract
OBJECTIVES Upper airway assessment requires a fully-automated segmentation system for complete or sub-regional identification. This study aimed to develop a novel Deep Learning (DL) model for accurate segmentation of the upper airway and achieve entire and subregional identification. METHODS Fifty cone-beam computed tomography (CBCT) scans, including 24,502 slices, were labelled as the ground truth by one orthodontist and two otorhinolaryngologists. A novel model, a lightweight multitask network based on the Swin Transformer and U-Net, was built for automatic segmentation of the entire upper airway and subregions. Segmentation performance was evaluated using Precision, Recall, Dice similarity coefficient (DSC) and Intersection over union (IoU). The clinical implications of the precision errors were quantitatively analysed, and comparisons between the AI model and Dolphin software were conducted. RESULTS Our model achieved good performance with a precision of 85.88-94.25%, recall of 93.74-98.44%, DSC of 90.95-96.29%, IoU of 83.68-92.85% in the overall and subregions of three-dimensional (3D) upper airway, and a precision of 91.22-97.51%, recall of 90.70-97.62%, DSC of 90.92-97.55%, and IoU of 83.41-95.29% in the subregions of two-dimensional (2D) crosssections. Discrepancies in volume and area caused by precision errors did not affect clinical outcomes. Both our AI model and the Dolphin software provided clinically acceptable consistency for pharyngeal airway assessments. CONCLUSION The novel DL model not only achieved segmentation of the entire upper airway, including the nasal cavity and subregion identification, but also performed exceptionally well, making it well suited for 3D upper airway assessment from the nasal cavity to the hypopharynx, especially for intricate structures. CLINICAL SIGNIFICANCE This system provides insights into the aetiology, risk, severity, treatment effect, and prognosis of dentoskeletal deformities and obstructive sleep apnea. It achieves rapid assessment of the entire upper airway and its subregions, making airway management-an integral part of orthodontic treatment, orthognathic surgery, and ENT surgery-easier.
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Affiliation(s)
- Suhan Jin
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China; Department of Orthodontics, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi, China
| | - Haojie Han
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China
| | - Zhiqun Huang
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuandi Xiang
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingyuan Du
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China
| | - Fang Hua
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China
| | - Xiaoyan Guan
- Department of Orthodontics, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi, China
| | - Jianguo Liu
- School of Stomatology, Zunyi Medical University, Zunyi, China; Special Key Laboratory of Oral Diseases Research, Higher Education Institution, Zunyi, China
| | - Fang Chen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.
| | - Hong He
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China.
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Chu G, Zhang R, He Y, Ng CH, Gu M, Leung YY, He H, Yang Y. Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images. Bioengineering (Basel) 2023; 10:915. [PMID: 37627800 PMCID: PMC10451171 DOI: 10.3390/bioengineering10080915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023] Open
Abstract
OBJECTIVE To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. MATERIALS AND METHODS Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. RESULTS The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. CONCLUSIONS We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images.
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Affiliation(s)
- Guang Chu
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Rongzhao Zhang
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Yingqing He
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Chun Hown Ng
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Min Gu
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Yiu Yan Leung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Hong He
- Department of Orthodontics, The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST), Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan 430072, China
| | - Yanqi Yang
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
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12
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Maken P, Gupta A, Gupta MK. A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images. Med Biol Eng Comput 2023; 61:1901-1927. [PMID: 37248380 DOI: 10.1007/s11517-023-02842-x] [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: 09/22/2022] [Accepted: 04/20/2023] [Indexed: 05/31/2023]
Abstract
The human upper airway is comprised of many anatomical volumes. The obstructions in the upper airway volumes are needed to be diagnosed which requires volumetric segmentation. Manual segmentation is time-consuming and requires expertise in the field. Automatic segmentation provides reliable results and also saves time and effort for the expert. The objective of this study is to systematically review the literature to study various techniques used for the automatic segmentation of the human upper airway regions in volumetric images. PRISMA guidelines were followed to conduct the systematic review. Four online databases Scopus, Google Scholar, PubMed, and JURN were used for the searching of the relevant papers. The relevant papers were shortlisted using inclusion and exclusion eligibility criteria. Three review questions were made and explored to find their answers. The best technique among all the literature studies based on the Dice coefficient and precision was identified and justified through the analysis. This systematic review provides insight to the researchers so that they shall be able to overcome the prominent issues in the field identified from the literature. The outcome of the review is based on several parameters, e.g., accuracy, techniques, challenges, datasets, and segmentation of different sub-regions. Flowchart of the search process as per PRISMA guidelines along with inclusion and exclusion criteria.
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Affiliation(s)
- Payal Maken
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Abhishek Gupta
- Biomedical Application Division, CSIR-Central Scientific Instruments Organisation, Chandigarh, 160030, India.
| | - Manoj Kumar Gupta
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, India
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Fan W, Zhang J, Wang N, Li J, Hu L. The Application of Deep Learning on CBCT in Dentistry. Diagnostics (Basel) 2023; 13:2056. [PMID: 37370951 DOI: 10.3390/diagnostics13122056] [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: 05/11/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user's proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.
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Affiliation(s)
- Wenjie Fan
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiaqi Zhang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Nan Wang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jia Li
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Hu
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Kim DY, Woo S, Roh JY, Choi JY, Kim KA, Cha JY, Kim N, Kim SJ. Subregional pharyngeal changes after orthognathic surgery in skeletal Class III patients analyzed by convolutional neural networks-based segmentation. J Dent 2023:104565. [PMID: 37308053 DOI: 10.1016/j.jdent.2023.104565] [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: 01/24/2023] [Revised: 05/03/2023] [Accepted: 05/27/2023] [Indexed: 06/14/2023] Open
Abstract
OBJECTIVES To evaluate the accuracy of fully automatic segmentation of pharyngeal volume of interests (VOIs) before and after orthognathic surgery in skeletal Class III patients using a convolutional neural network (CNN) model and to investigate the clinical applicability of artificial intelligence for quantitative evaluation of treatment changes in pharyngeal VOIs. METHODS 310 cone-beam computed tomography (CBCT) images were divided into a training set (n=150), validation set (n=40), and test set (n=120). The test datasets comprised matched pairs of pre- and posttreatment images of 60 skeletal Class III patients (mean age 23.1±5.0 years; ANB<-2⁰) who underwent bimaxillary orthognathic surgery with orthodontic treatment. A 3D U-Net CNNs model was applied for fully automatic segmentation and measurement of subregional pharyngeal volumes of pretreatment (T0) and posttreatment (T1) scans. The model's accuracy was compared to semi-automatic segmentation outcomes by humans using the dice similarity coefficient (DSC) and volume similarity (VS). The correlation between surgical skeletal changes and model accuracy was obtained. RESULTS The proposed model achieved high performance of subregional pharyngeal segmentation on both T0 and T1 images, representing a significant T1-T0 difference of DSC only in the nasopharynx. Region-specific differences among pharyngeal VOIs, which were observed at T0, disappeared on the T1 images. The decreased DSC of nasopharyngeal segmentation after treatment was weakly correlated with the amount of maxillary advancement. There was no correlation between the mandibular setback amount and model accuracy. CONCLUSIONS The proposed model offers fast and accurate subregional pharyngeal segmentation on both pretreatment and posttreatment CBCT images in skeletal Class III patients. CLINICAL SIGNIFICANCE We elucidated the clinical applicability of the CNNs model to quantitatively evaluate subregional pharyngeal changes after surgical-orthodontic treatment, which offers a basis for developing a fully integrated multiclass CNNs model to predict pharyngeal responses after dentoskeletal treatments.
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Affiliation(s)
- Dong-Yul Kim
- Department of Dentistry, Graduate School, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Seoyeon Woo
- Department of Convergence Medicine, Asan Medical Institute of Convergence, Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jae-Yon Roh
- Department of Dentistry, Graduate School, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Jin-Young Choi
- Department of Orthodontics, Kyung Hee University Dental Hospital, 23, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Kyung-A Kim
- Department of Orthodontics, School of Dentistry, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Jung-Yul Cha
- Department of Orthodontics, The Institute of Craniofacial Deformity, College of Dentistry, Yonsei University, 50-1 Yonseiro, Seodaemun-gu, Seoul, 03722, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Su-Jung Kim
- Department of Orthodontics, School of Dentistry, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea.
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Uzun Saylan BC, Baydar O, Yeşilova E, Kurt Bayrakdar S, Bilgir E, Bayrakdar İŞ, Çelik Ö, Orhan K. Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study. Diagnostics (Basel) 2023; 13:diagnostics13101800. [PMID: 37238284 DOI: 10.3390/diagnostics13101800] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/13/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
The assessment of alveolar bone loss, a crucial element of the periodontium, plays a vital role in the diagnosis of periodontitis and the prognosis of the disease. In dentistry, artificial intelligence (AI) applications have demonstrated practical and efficient diagnostic capabilities, leveraging machine learning and cognitive problem-solving functions that mimic human abilities. This study aims to evaluate the effectiveness of AI models in identifying alveolar bone loss as present or absent across different regions. To achieve this goal, alveolar bone loss models were generated using the PyTorch-based YOLO-v5 model implemented via CranioCatch software, detecting periodontal bone loss areas and labeling them using the segmentation method on 685 panoramic radiographs. Besides general evaluation, models were grouped according to subregions (incisors, canines, premolars, and molars) to provide a targeted evaluation. Our findings reveal that the lowest sensitivity and F1 score values were associated with total alveolar bone loss, while the highest values were observed in the maxillary incisor region. It shows that artificial intelligence has a high potential in analytical studies evaluating periodontal bone loss situations. Considering the limited amount of data, it is predicted that this success will increase with the provision of machine learning by using a more comprehensive data set in further studies.
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Affiliation(s)
- Bilge Cansu Uzun Saylan
- Department of Periodontology, Faculty of Dentistry, Dokuz Eylul University, İzmir 35220, Turkey
| | - Oğuzhan Baydar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ege University, İzmir 35040, Turkey
| | - Esra Yeşilova
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Sevda Kurt Bayrakdar
- Department of Periodontology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Elif Bilgir
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26480, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06830, Turkey
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16
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Gardiyanoğlu E, Ünsal G, Akkaya N, Aksoy S, Orhan K. Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls. Diagnostics (Basel) 2023; 13:diagnostics13081487. [PMID: 37189586 DOI: 10.3390/diagnostics13081487] [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: 12/22/2022] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs). METHODS 8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique. RESULTS The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively. CONCLUSIONS Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.
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Affiliation(s)
- Emel Gardiyanoğlu
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Gürkan Ünsal
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
- DESAM Institute, Near East University, 99138 Nicosia, Cyprus
| | - Nurullah Akkaya
- Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, 99138 Nicosia, Cyprus
| | - Seçil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, 06560 Ankara, Turkey
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Dong W, Chen Y, Li A, Mei X, Yang Y. Automatic detection of adenoid hypertrophy on cone-beam computed tomography based on deep learning. Am J Orthod Dentofacial Orthop 2023; 163:553-560.e3. [PMID: 36990529 DOI: 10.1016/j.ajodo.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 03/29/2023]
Abstract
INTRODUCTION This study proposed an automatic diagnosis method based on deep learning for adenoid hypertrophy detection on cone-beam computed tomography. METHODS The hierarchical masks self-attention U-net (HMSAU-Net) for segmentation of the upper airway and the 3-dimensional (3D)-ResNet for diagnosing adenoid hypertrophy were constructed on the basis of 87 cone-beam computed tomography samples. A self-attention encoder module was added to the SAU-Net to optimize upper airway segmentation precision. The hierarchical masks were introduced to ensure that the HMSAU-Net captured sufficient local semantic information. RESULTS We used Dice to evaluate the performance of HMSAU-Net and used diagnostic method indicators to test the performance of 3D-ResNet. The average Dice value of our proposed model was 0.960, which was superior to the 3DU-Net and SAU-Net models. In the diagnostic models, 3D-ResNet10 had an excellent ability to diagnose adenoid hypertrophy automatically with a mean accuracy of 0.912, a mean sensitivity of 0.976, a mean specificity of 0.867, a mean positive predictive value of 0.837, a mean negative predictive value of 0.981, and a F1 score of 0.901. CONCLUSIONS The value of this diagnostic system lies in that it provides a new method for the rapid and accurate early clinical diagnosis of adenoid hypertrophy in children, allows us to look at the upper airway obstruction in three-dimensional space and relieves the work pressure of imaging doctors.
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Affiliation(s)
- Wenjie Dong
- Department of Stomatology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yaosen Chen
- Department of Stomatology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Ankang Li
- Computer Science School, Wuhan University, Wuhan, Hubei, China
| | - Xiaoguang Mei
- Electronic Information School, Wuhan University, Wuhan, Hubei, China
| | - Yan Yang
- Department of Stomatology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
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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: 10] [Impact Index Per Article: 10.0] [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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de Bataille C, Bernard D, Dumoncel J, Vaysse F, Cussat-Blanc S, Telmon N, Maret D, Monsarrat P. Machine Learning Analysis of the Anatomical Parameters of the Upper Airway Morphology: A Retrospective Study from Cone-Beam CT Examinations in a French Population. J Clin Med 2022; 12:84. [PMID: 36614885 PMCID: PMC9820916 DOI: 10.3390/jcm12010084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/12/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
The objective of this study is to assess, using cone-beam CT (CBCT) examinations, the correlation between hard and soft anatomical parameters and their impact on the characteristics of the upper airway using symbolic regression as a machine learning strategy. Methods: On each CBCT, the upper airway was segmented, and 24 anatomical landmarks were positioned to obtain six angles and 19 distances. Some anatomical landmarks were related to soft tissues and others were related to hard tissues. To explore which variables were the most influential to explain the morphology of the upper airway, principal component and symbolic regression analyses were conducted. Results: In total, 60 CBCT were analyzed from subjects with a mean age of 39.5 ± 13.5 years. The intra-observer reproducibility for each variable was between good and excellent. The horizontal soft palate measure mostly contributed to the reduction of the airway volume and minimal section area with a variable importance of around 50%. The tongue and the position of the hyoid bone were also linked to the upper airway morphology. For hard anatomical structures, the anteroposterior position of the mandible and the maxilla had some influence. Conclusions: Although the volume of the airway is not accessible on all CBCT scans performed by dental practitioners, this study demonstrates that a small number of anatomical elements may be markers of the reduction of the upper airway with, potentially, an increased risk of obstructive sleep apnea. This could help the dentist refer the patient to a suitable physician.
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Affiliation(s)
- Caroline de Bataille
- Laboratoire Centre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, 31073 Toulouse, France
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
| | - David Bernard
- Institute of Research in Informatics (IRIT) of Toulouse, CNRS—UMR5505, 31062 Toulouse, France
- RESTORE Research Center, Department of Oral Medicine, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, Toulouse University Hospital (CHU), Batiment INCERE, 4bis Avenue Hubert Curien, 31100 Toulouse, France
| | - Jean Dumoncel
- Laboratoire Centre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, 31073 Toulouse, France
| | - Frédéric Vaysse
- Laboratoire Centre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, 31073 Toulouse, France
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
| | - Sylvain Cussat-Blanc
- Institute of Research in Informatics (IRIT) of Toulouse, CNRS—UMR5505, 31062 Toulouse, France
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
| | - Norbert Telmon
- Laboratoire Centre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, 31073 Toulouse, France
- Service de Médecine Légale, Centre Hospitalier Universitaire Rangueil, Avenue du Professeur Jean Poulhès, CEDEX 9, 31059 Toulouse, France
| | - Delphine Maret
- Laboratoire Centre d’Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, 31073 Toulouse, France
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
| | - Paul Monsarrat
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- RESTORE Research Center, Department of Oral Medicine, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, Toulouse University Hospital (CHU), Batiment INCERE, 4bis Avenue Hubert Curien, 31100 Toulouse, France
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
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Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111894. [PMID: 36431029 PMCID: PMC9696726 DOI: 10.3390/life12111894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/10/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND The use of artificial intelligence (AI) in health sciences is becoming increasingly popular among doctors nowadays. This study evaluated the literature regarding the use of AI for CBCT airway analysis. To our knowledge, this is the first systematic review that examines the performance of artificial intelligence in CBCT airway analysis. METHODS Electronic databases and the reference lists of the relevant research papers were searched for published and unpublished literature. Study selection, data extraction, and risk of bias evaluation were all carried out independently and twice. Finally, five articles were chosen. RESULTS The results suggested a high correlation between the automatic and manual airway measurements indicating that the airway measurements may be automatically and accurately calculated from CBCT images. CONCLUSIONS According to the present literature, automatic airway segmentation can be used for clinical purposes. The main key findings of this systematic review are that the automatic airway segmentation is accurate in the measurement of the airway and, at the same time, appears to be fast and easy to use. However, the present literature is really limited, and more studies in the future providing high-quality evidence are needed.
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Cho HN, Gwon E, Kim KA, Baek SH, Kim N, Kim SJ. Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern. Am J Orthod Dentofacial Orthop 2022; 162:e53-e62. [PMID: 35654686 DOI: 10.1016/j.ajodo.2022.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional neural networks model for the fully automatic segmentation of regional pharyngeal volume of interests (VOIs) in cone-beam computed tomography scans to compare the accuracy of the model performance across different skeletal patterns presenting with various pharyngeal dimensions. METHODS Two-hundred sixteen cone-beam computed tomography scans of adult patients were randomly divided into training (n = 100), validation (n = 16), and test (n = 100) datasets. We trained the 3D U-Net model for fully automatic segmentation of pharyngeal VOIs and their measurements: nasopharyngeal, velopharyngeal, glossopharyngeal, and hypopharyngeal sections as well as total pharyngeal airway space (PAS). The test datasets were subdivided according to the sagittal and vertical skeletal patterns. The segmentation performance was assessed by dice similarity coefficient, volumetric similarity, precision, and recall values, compared with the ground truth created by 1 expert's manual processing using semiautomatic software. RESULTS The proposed model achieved highly accurate performance, showing a mean dice similarity coefficient of 0.928 ± 0.023, the volumetric similarity of 0.928 ± 0.023, precision of 0.925 ± 0.030, and recall of 0.921 ± 0.029 for total PAS segmentation. The performance showed region-specific differences, revealing lower accuracy in the glossopharyngeal and hypopharyngeal sections than in the upper sections (P <0.001). However, the accuracy of model performance at each pharyngeal VOI showed no significant difference according to sagittal or vertical skeletal patterns. CONCLUSIONS The 3D-convolutional neural network performance for region-specific PAS analysis is promising to substitute for laborious and time-consuming manual analysis in every skeletal and pharyngeal pattern.
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Affiliation(s)
- Ha-Nul Cho
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Eunseo Gwon
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Kyung-A Kim
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Radiology, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Su-Jung Kim
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea.
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Orhan K, Shamshiev M, Ezhov M, Plaksin A, Kurbanova A, Ünsal G, Gusarev M, Golitsyna M, Aksoy S, Mısırlı M, Rasmussen F, Shumilov E, Sanders A. AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients. Sci Rep 2022; 12:11863. [PMID: 35831451 PMCID: PMC9279304 DOI: 10.1038/s41598-022-15920-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 07/01/2022] [Indexed: 11/21/2022] Open
Abstract
This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement.
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Affiliation(s)
- Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey. .,Medical Design Application, and Research Center (MEDITAM), Ankara University, Ankara, Turkey. .,Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, Lublin, Poland.
| | | | | | | | - Aida Kurbanova
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | - Gürkan Ünsal
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus.,Research Center of Experimental Health Science (DESAM), Near East University, Nicosia, Cyprus
| | | | | | - Seçil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | - Melis Mısırlı
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | - Finn Rasmussen
- Internal Medicine Department Lunge Section, SVS Esbjerg, Esbjerg, Denmark.,Life Lung Health Center, Nicosia, Cyprus
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Lo Giudice A, Ronsivalle V, Gastaldi G, Leonardi R. Assessment of the accuracy of imaging software for 3D rendering of the upper airway, usable in orthodontic and craniofacial clinical settings. Prog Orthod 2022; 23:22. [PMID: 35691961 PMCID: PMC9189077 DOI: 10.1186/s40510-022-00413-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several semi-automatic software are available for the three-dimensional reconstruction of the airway from DICOM files. The aim of this study was to evaluate the accuracy of the segmentation of the upper airway testing four free source and one commercially available semi-automatic software. A total of 20 cone-beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the upper airway. The software tested were Invesalius, ITK-Snap, Dolphin 3D, 3D Slicer and Seg3D. The same upper airway models were manually segmented (Mimics software) and set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the upper airway model obtained with semi-automatic software and the GS model, and to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the upper airway models compared to the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed for software comparisons. RESULTS Statistically significant differences were found in the volumetric dimensions of the upper airway models and in the matching percentage among the tested software (p < 0.001). Invesalius was the most accurate software for 3D rendering of the upper airway (mean bias = 1.54 cm3; matching = 90.05%) followed by ITK-Snap (mean bias = - 2.52 cm3; matching = 84.44%), Seg 3D (mean bias = 3.21 cm3, matching = 87.36%), 3D Slicer (mean bias = - 4.77 cm3; matching = 82.08%) and Dolphin 3D (difference mean = - 6.06 cm3; matching = 78.26%). According to the color-coded map, the dis-matched area was mainly located at the most anterior nasal region of the airway. Volumetric data showed excellent inter-software reliability (GS vs semi-automatic software), with coefficient values ranging from 0.904 to 0.993, confirming proportional equivalence with manual segmentation. CONCLUSION Despite the excellent inter-software reliability, different semi-automatic segmentation algorithms could generate different patterns of inaccuracy error (underestimation/overestimation) of the upper airway models. Thus, is unreasonable to expect volumetric agreement among different software packages for the 3D rendering of the upper airway anatomy.
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Affiliation(s)
- Antonino Lo Giudice
- Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy.
| | - Vincenzo Ronsivalle
- Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy
| | - Giorgio Gastaldi
- Department Orthodontics, Vita-Salute San Raffaele University, Milan, Italy
| | - Rosalia Leonardi
- Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Unit of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy
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Digital Technologies: From Scientific to Clinical Applications in Orthodontic and Dental Communities. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12104977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The significant progress made in our sector since the introduction of digital technologies has now made it possible to easily obtain all the information necessary to diagnose, design and perform interdisciplinary and complex therapies in a simpler and more reproducible way [...]
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25
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Leonardi RM. 3D Imaging Advancements and New Technologies in Clinical and Scientific Dental and Orthodontic Fields. J Clin Med 2022; 11:jcm11082200. [PMID: 35456293 PMCID: PMC9031999 DOI: 10.3390/jcm11082200] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 02/01/2023] Open
Affiliation(s)
- Rosalia Maria Leonardi
- Department of Medical-Surgical Specialties-Section of Orthodontics, School of Dentistry, University of Catania, Policlinico Universitario "G. Rodolico- San Marco", Via Santa Sofia 78, 95123 Catania, Italy
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Almajalid R, Zhang M, Shan J. Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI. Diagnostics (Basel) 2022; 12:123. [PMID: 35054290 PMCID: PMC8774512 DOI: 10.3390/diagnostics12010123] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/24/2021] [Accepted: 12/30/2021] [Indexed: 02/06/2023] Open
Abstract
In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model's performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.
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Affiliation(s)
- Rania Almajalid
- Department of Computer Science, Seidenberg School of CSIS, Pace University, New York, NY 10038, USA;
- College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia
| | - Ming Zhang
- Department of Computer Science & Networking, Wentworth Institute of Technology, Boston, MA 02115, USA
- Division of Rheumatology, Tufts Medical Center, Boston, MA 02111, USA
| | - Juan Shan
- Department of Computer Science, Seidenberg School of CSIS, Pace University, New York, NY 10038, USA;
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Badr FF, Jadu FM. Performance of artificial intelligence using oral and maxillofacial CBCT images: A systematic review and meta-analysis. Niger J Clin Pract 2022; 25:1918-1927. [DOI: 10.4103/njcp.njcp_394_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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28
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Shetty SR, Al Bayatti SW, Al-Rawi NH, Kamath V, Reddy S, Narasimhan S, Al Kawas S, Madi M, Achalli S, Bhat S. The effect of concha bullosa and nasal septal deviation on palatal dimensions: a cone beam computed tomography study. BMC Oral Health 2021; 21:607. [PMID: 34814910 PMCID: PMC8609805 DOI: 10.1186/s12903-021-01974-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/15/2021] [Indexed: 11/10/2022] Open
Abstract
Introduction Nasal septal deviation (NSD) and concha bullosa (CB) are associated with airway obstruction in mouth breathers. Mouth breathing is associated with alterations in maxillary growth and palatal architecture. The aim of our study was to determine the effect of the presence of CB and NSD on the dimensions of the hard palate using cone-beam computed tomography (CBCT). Materials and methods A retrospective study was conducted using CBCT scans of 200 study subjects. The study subjects were divided into four groups based on the presence of CB and NSD. Septal deviation angle (SDA), palatal interalveolar length (PIL), palatal depth (PD) and maxillopalatal arch angle (MPAA) were measured in the study groups. Results The presence of NSD and CB was associated with significant (p < 0.001) differences in the palatal dimensions of the study subjects. The PIL and MPA (p < 0.001) were significantly reduced (p < 0.001), whereas the PD was significantly increased (p < 0.001) in study subjects with NSD and CB. There was no significant change in the palatal dimensions between the unilateral and bilateral types of CB. Among the palatal dimensions, the PIL had the most significant association (R2 = 0.53) with SDA and CB. There was a significant correlation between the palatal dimensions and SDA when CB was present along with NSD. Conclusion Based on the results of this study, it can be concluded that the presence of NSD and CB have a significant effect on the palatal dimensions and, therefore, they may be associated with skeletal malocclusion.
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Affiliation(s)
- Shishir Ram Shetty
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
| | - Saad Wahby Al Bayatti
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Natheer Hashim Al-Rawi
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | | | - Sesha Reddy
- College of Dentistry, Gulf Medical University, Ajman, United Arab Emirates
| | - Sangeetha Narasimhan
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Sausan Al Kawas
- Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Medhini Madi
- Manipal College of Dental Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Sonika Achalli
- A B Shetty Memorial Institute of Dental Sciences, Nitte Deemed to be University, Mangalore, India
| | - Supriya Bhat
- A B Shetty Memorial Institute of Dental Sciences, Nitte Deemed to be University, Mangalore, India
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Leonardi R, Giudice AL, Isola G, Spampinato C. Deep learning and computer vision: Two promising pillars, powering the future in orthodontics. Semin Orthod 2021. [DOI: 10.1053/j.sodo.2021.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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