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Xiang B, Lu J, Yu J. Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis. J Dent 2024; 146:105064. [PMID: 38768854 DOI: 10.1016/j.jdent.2024.105064] [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: 11/10/2023] [Revised: 04/22/2024] [Accepted: 05/09/2024] [Indexed: 05/22/2024] Open
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques. DATA The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC). SOURCES PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques. CONCLUSIONS AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies. CLINICAL SIGNIFICANCE AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice.
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Affiliation(s)
- Bilu Xiang
- School of Dentistry, Shenzhen University Medical School, Shenzhen University, Shenzhen 518000, China.
| | - Jiayi Lu
- Department of Stomatology, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518000, China
| | - Jiayi Yu
- Department of Stomatology, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518000, China
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Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus 2024; 16:e62045. [PMID: 38989357 PMCID: PMC11234326 DOI: 10.7759/cureus.62045] [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: 03/03/2024] [Accepted: 06/09/2024] [Indexed: 07/12/2024] Open
Abstract
Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.
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Affiliation(s)
- Aathira Surendran
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Pallavi Daigavane
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Sunita Shrivastav
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Ranjit Kamble
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Abhishek D Sanchla
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Lovely Bharti
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Mrudula Shinde
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
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Salazar D, Rossouw PE, Javed F, Michelogiannakis D. Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review. J Orthod 2024; 51:107-119. [PMID: 37772513 DOI: 10.1177/14653125231203743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
BACKGROUND The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed. OBJECTIVES To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT. DESIGN Systematic review. DATA SOURCES Unrestricted search of indexed databases and reference lists of included studies. DATA SELECTION Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included. DATA EXTRACTION Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively. DATA SYNTHESIS Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB. LIMITATIONS Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment. CONCLUSION AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT. REGISTRATION PROSPERO CRD42022366864.
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Affiliation(s)
- Daisy Salazar
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Paul Emile Rossouw
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Fawad Javed
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Dimitrios Michelogiannakis
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
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Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review. Bioengineering (Basel) 2024; 11:431. [PMID: 38790300 PMCID: PMC11118054 DOI: 10.3390/bioengineering11050431] [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: 03/12/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.
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Affiliation(s)
| | - Shayan Darvish
- School of Dentistry, University of Michigan, Ann Arbor, MI 48104, USA;
| | - Bahareh Nazemi Salman
- Department of Pediatric Dentistry, School of Dentistry, Zanjan University of Medical Sciences, Zanjan 4513956184, Iran
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
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Hatia A, Doldo T, Parrini S, Chisci E, Cipriani L, Montagna L, Lagana G, Guenza G, Agosta E, Vinjolli F, Hoxha M, D’Amelio C, Favaretto N, Chisci G. Accuracy and Completeness of ChatGPT-Generated Information on Interceptive Orthodontics: A Multicenter Collaborative Study. J Clin Med 2024; 13:735. [PMID: 38337430 PMCID: PMC10856539 DOI: 10.3390/jcm13030735] [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: 01/10/2024] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Background: this study aims to investigate the accuracy and completeness of ChatGPT in answering questions and solving clinical scenarios of interceptive orthodontics. Materials and Methods: ten specialized orthodontists from ten Italian postgraduate orthodontics schools developed 21 clinical open-ended questions encompassing all of the subspecialities of interceptive orthodontics and 7 comprehensive clinical cases. Questions and scenarios were inputted into ChatGPT4, and the resulting answers were evaluated by the researchers using predefined accuracy (range 1-6) and completeness (range 1-3) Likert scales. Results: For the open-ended questions, the overall median score was 4.9/6 for the accuracy and 2.4/3 for completeness. In addition, the reviewers rated the accuracy of open-ended answers as entirely correct (score 6 on Likert scale) in 40.5% of cases and completeness as entirely correct (score 3 n Likert scale) in 50.5% of cases. As for the clinical cases, the overall median score was 4.9/6 for accuracy and 2.5/3 for completeness. Overall, the reviewers rated the accuracy of clinical case answers as entirely correct in 46% of cases and the completeness of clinical case answers as entirely correct in 54.3% of cases. Conclusions: The results showed a high level of accuracy and completeness in AI responses and a great ability to solve difficult clinical cases, but the answers were not 100% accurate and complete. ChatGPT is not yet sophisticated enough to replace the intellectual work of human beings.
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Affiliation(s)
- Arjeta Hatia
- Orthodontics Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (T.D.); (L.C.)
| | - Tiziana Doldo
- Orthodontics Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (T.D.); (L.C.)
| | - Stefano Parrini
- Oral Surgery Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy;
| | - Elettra Chisci
- Orthodontics Postgraduate School, University of Ferrara, 44121 Ferrara, Italy
| | - Linda Cipriani
- Orthodontics Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (T.D.); (L.C.)
| | - Livia Montagna
- Orthodontics Postgraduate School, University of Cagliari, 09121 Cagliari, Italy;
| | - Giuseppina Lagana
- Orthodontics Postgraduate School, “Sapienza” University of Rome, 00185 Rome, Italy;
| | - Guia Guenza
- Orthodontics Postgraduate School, University of Milano, 20019 Milan, Italy
| | - Edoardo Agosta
- Orthodontics Postgraduate School, University of Torino, 10024 Turin, Italy
| | - Franceska Vinjolli
- Orthodontics Postgraduate School, University of Roma Tor Vergata, 00133 Rome, Italy;
| | - Meladiona Hoxha
- Orthodontics Postgraduate School, “Cattolica” University of Rome, 00168 Rome, Italy;
| | - Claudio D’Amelio
- Orthodontics Postgraduate School, University of Chieti, 66100 Chieti, Italy;
| | - Nicolò Favaretto
- Orthodontics Postgraduate School, University of Trieste, 34100 Trieste, Italy
| | - Glauco Chisci
- Oral Surgery Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy;
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Akl HE, Mostafa YA. Digitization and validation of the open bite checklist manifesto: a step toward artificial intelligence. Angle Orthod 2024; 94:51-58. [PMID: 37650552 DOI: 10.2319/032923-225.1] [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: 03/01/2023] [Accepted: 07/01/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVES To introduce and validate newly designed computer software to aid in the diagnosis of anterior open bite (AOB). MATERIALS AND METHODS The software was constructed based on the algorithm of a standardized open bite checklist, which considered skeletal, dental, and soft tissue components, as well as smile characteristics. Feeding the software with this input yielded a digital form output (DFO) in the guise of a diagnostic report characterizing the AOB phenotype, contributing components, severity, associated problems, and functional factors. For validation, DFO was compared to a conventional form output (CFO), created in a standardized manner according to expert opinions. Agreement between the DFO and CFO in terms of AOB phenotype was the primary outcome, while the secondary outcome was the number of missing diagnostic components in either method. RESULTS Percentage of agreement between CFO and DFO was 82.2%, with a kappa coefficient of 0.78, which is considered a good level of agreement. There was a statistically significant relationship between the number of missing diagnostic components in CFO and level of disagreement, which rendered the DFO more reliable. CONCLUSIONS Newly constructed software represents an efficient and valid diagnostic tool for AOB and its contributing components. There was good agreement between CFO and DFO, with the latter being more comprehensive and reliable. The algorithm built in the software can be used as the basis for a future artificial intelligence model to aid in the diagnosis of AOB.
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Allareddy V, Rampa S, Venugopalan SR, Elnagar MH, Lee MK, Oubaidin M, Yadav S. Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health. Orthod Craniofac Res 2023; 26 Suppl 1:118-123. [PMID: 37036565 DOI: 10.1111/ocr.12662] [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: 02/23/2023] [Revised: 03/25/2023] [Accepted: 03/29/2023] [Indexed: 04/11/2023]
Abstract
There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.
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Affiliation(s)
- Veerasathpurush Allareddy
- Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | | | | | - Mohammed H Elnagar
- University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Min Kyeong Lee
- University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Maysaa Oubaidin
- University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA
| | - Sumit Yadav
- UNMC College of Dentistry, Lincoln, Nebraska, USA
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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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9
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Fawaz P, Sayegh PE, Vannet BV. What is the current state of artificial intelligence applications in dentistry and orthodontics? JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2023; 124:101524. [PMID: 37270174 DOI: 10.1016/j.jormas.2023.101524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/08/2023] [Accepted: 05/31/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from. TYPES OF STUDIES REVIEWED Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies. RESULTS The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable. PRACTICAL IMPLICATIONS AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.
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Affiliation(s)
- Paul Fawaz
- Academic Lecturer & Researcher at the Orthodontic department Université de Lorraine, Nancy, France.
| | | | - Bart Vande Vannet
- Clinical and Academical responsable of the Orthodontic department at Université de Lorraine, Nancy, France.
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Wong KF, Lam XY, Jiang Y, Yeung AWK, Lin Y. Artificial intelligence in orthodontics and orthognathic surgery: a bibliometric analysis of the 100 most-cited articles. Head Face Med 2023; 19:38. [PMID: 37612673 PMCID: PMC10463886 DOI: 10.1186/s13005-023-00383-0] [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: 07/25/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND The application of artificial intelligence (AI) in orthodontics and orthognathic surgery has gained significant attention in recent years. However, there is a lack of bibliometric reports that analyze the academic literature in this field to identify publishing and citation trends. By conducting an analysis of the top 100 most-cited articles on AI in orthodontics and orthognathic surgery, we aim to unveil popular research topics, key authors, institutions, countries, and journals in this area. METHODS A comprehensive search was conducted in the Web of Science (WOS) electronic database to identify the top 100 most-cited articles on AI in orthodontics and orthognathic surgery. Publication and citation data were obtained and further analyzed and visualized using R Biblioshiny. The key domains of the 100 articles were also identified. RESULTS The top 100 most-cited articles were published between 2005 and 2022, contributed by 458 authors, with an average citation count of 22.09. South Korea emerged as the leading contributor with the highest number of publications (28) and citations (595), followed by China (16, 373), and the United States (7, 248). Notably, six South Korean authors ranked among the top 10 contributors, and three South Korean institutions were listed as the most productive. International collaborations were predominantly observed between the United States, China, and South Korea. The main domains of the articles focused on automated imaging assessment (42%), aiding diagnosis and treatment planning (34%), and the assessment of growth and development (10%). Besides, a positive correlation was observed between the testing sample size and citation counts (P = 0.010), as well as between the time of publication and citation counts (P < 0.001). CONCLUSIONS The utilization of AI in orthodontics and orthognathic surgery has shown remarkable progress, particularly in the domains of imaging analysis, diagnosis and treatment planning, and growth and development assessment. This bibliometric analysis provides valuable insights into the top-cited articles and the trends of AI research in this field.
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Affiliation(s)
- Ka Fai Wong
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China
| | - Xiang Yao Lam
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China
| | - Yuhao Jiang
- Department of Restorative Dentistry, Faculty of Dentistry, the National University of Malaysia, Kuala Lumpur, Malaysia
| | - 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
| | - Yifan Lin
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China.
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Prasad S, Farella M. Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future. Clin Oral Investig 2023; 27:4861-4867. [PMID: 37410151 DOI: 10.1007/s00784-023-05127-7] [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: 09/13/2022] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.
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Affiliation(s)
- Sabarinath Prasad
- Department of Orthodontics, Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University, Dubai, United Arab Emirates.
| | - Mauro Farella
- Discipline of Orthodontics, Faculty of Dentistry, University of Otago, Dunedin, New Zealand
- Discipline of Orthodontics and Pediatric Dentistry, Department of Surgical Science, University of Cagliari, Cagliari, Italy
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12
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Huang H, Zheng O, Wang D, Yin J, Wang Z, Ding S, Yin H, Xu C, Yang R, Zheng Q, Shi B. ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model. Int J Oral Sci 2023; 15:29. [PMID: 37507396 PMCID: PMC10382494 DOI: 10.1038/s41368-023-00239-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
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Affiliation(s)
- Hanyao Huang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
| | - Ou Zheng
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA.
| | - Dongdong Wang
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA
| | - Jiayi Yin
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Zijin Wang
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA
| | - Shengxuan Ding
- College of Transportation Engineering, University of Central Florida, Orlando, USA
| | - Heng Yin
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Chuan Xu
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
- C2SMART Center, Tandon School of Engineering, New York University, Brooklyn, USA
| | - Renjie Yang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Eastern Clinic, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Qian Zheng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bing Shi
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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13
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Ayad N, Schwendicke F, Krois J, van den Bosch S, Bergé S, Bohner L, Hanisch M, Vinayahalingam S. Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey. Head Face Med 2023; 19:23. [PMID: 37349791 PMCID: PMC10288769 DOI: 10.1186/s13005-023-00368-z] [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: 01/05/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher's exact tests with Monte Carlo approximation. Patients' perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor-patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1-5 (42.3%) or 5-10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18-35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients' perceptions may allow professionals to shape AI-driven dentistry in the future.
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Affiliation(s)
- Nasim Ayad
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Stefanie van den Bosch
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Stefaan Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Lauren Bohner
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Marcel Hanisch
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
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14
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de Queiroz Tavares Borges Mesquita G, Vieira WA, Vidigal MTC, Travençolo BAN, Beaini TL, Spin-Neto R, Paranhos LR, de Brito Júnior RB. Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis. J Digit Imaging 2023; 36:1158-1179. [PMID: 36604364 PMCID: PMC10287619 DOI: 10.1007/s10278-022-00766-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/19/2022] [Accepted: 12/19/2022] [Indexed: 01/07/2023] Open
Abstract
Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I2 = 99%) and 90% (95% CI: 87-92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41-2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.
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Affiliation(s)
| | - Walbert A Vieira
- Department of Restorative Dentistry, Endodontics Division, School of Dentistry of Piracicaba, State University of Campinas, Piracicaba, São Paulo, Brazil
| | | | | | - Thiago Leite Beaini
- Department of Preventive and Community Dentistry, School of Dentistry, Federal University of Uberlândia, Campus Umuarama Av. Pará, 1720, Bloco 2G, sala 1, 38405-320, Uberlândia, Minas Gerais, Brazil
| | - Rubens Spin-Neto
- Department of Dentistry and Oral Health, Section for Oral Radiology, Aarhus University, Aarhus C, Denmark
| | - Luiz Renato Paranhos
- Department of Preventive and Community Dentistry, School of Dentistry, Federal University of Uberlândia, Campus Umuarama Av. Pará, 1720, Bloco 2G, sala 1, 38405-320, Uberlândia, Minas Gerais, Brazil.
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15
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Thirumoorthy S, Gopal S. Diagnostic accuracy of AI in orthodontic extraction decisions: "Are we ready to let Mr. Data run our Enterprise?" A commentary on a systematic review. Evid Based Dent 2023; 24:17-18. [PMID: 36890254 DOI: 10.1038/s41432-023-00865-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVE To collect evidence on the ability of artificial intelligence programs to accurately make extraction decisions in orthodontic treatment planning. DATA SOURCES Authors electronically searched the following databases: PubMed/ MEDLINE, EMBASE, LILACS, Web of Science, Scopus, LIVIVO, Computers & Applied Science, ACM Digital Library, and Compendex, Open Grey, Google Scholar, and ProQuest Dissertation and Thesis. STUDY SELECTION Three independent reviewers collected the following data: number of cases of extraction and non-extraction, number of experts in orthodontics and their years of experience, number of variables used in the index model test, type of artificial intelligence and algorithms, accuracy outcomes, the three highest variable ranks weighted in the computational model, and the main conclusion. DATA EXTRACTION AND SYNTHESIS Risk of bias was assessed using Quadas 2 checklist for AI, and certainty of evidence was evaluated by GRADE. RESULTS After 2 phases of screening by 3 independent reviewers, 6 studies met the inclusion criteria for the final review. The AI programs used by the included studies were as follows: ensemble learning/random forest, artificial neural network/multilayer perceptron, machine learning/back propagation and machine learning/feature vectors. All studies showed an unclear risk of bias for patient selection. Two studies had high risk of bias in the index test, while two others presented an unclear risk of bias in the diagnostic test. Meta-analysis of the pooled data resulted in 0.87 accuracy value for all studies. CONCLUSIONS The authors conclude that AI's ability to predict extractions is promising but should be interpreted with caution.
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Affiliation(s)
- Soumya Thirumoorthy
- Board Certified Orthodontist, Smilelife Orthodontics, Corpus Christi, TX, USA.
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16
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Mohammad-Rahimi H, Rokhshad R, Bencharit S, Krois J, Schwendicke F. Deep learning: A primer for dentists and dental researchers. J Dent 2023; 130:104430. [PMID: 36682721 DOI: 10.1016/j.jdent.2023.104430] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVES Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes. METHODS Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study. RESULTS In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project. CONCLUSION Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects. CLINICAL SIGNIFICANCE This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.
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Affiliation(s)
- Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | - Rata Rokhshad
- Department of Medicine, Section of Endocrinology, Nutrition, and Diabetes, Vitamin D, Boston University Medical Center, Boston, MA, USA
| | - Sompop Bencharit
- Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, School of Dentistry, and Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Federal Republic of Germany.
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Strunga M, Urban R, Surovková J, Thurzo A. Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment. Healthcare (Basel) 2023; 11:healthcare11050683. [PMID: 36900687 PMCID: PMC10000479 DOI: 10.3390/healthcare11050683] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
This scoping review examines the contemporary applications of advanced artificial intelligence (AI) software in orthodontics, focusing on its potential to improve daily working protocols, but also highlighting its limitations. The aim of the review was to evaluate the accuracy and efficiency of current AI-based systems compared to conventional methods in diagnosing, assessing the progress of patients' treatment and follow-up stability. The researchers used various online databases and identified diagnostic software and dental monitoring software as the most studied software in contemporary orthodontics. The former can accurately identify anatomical landmarks used for cephalometric analysis, while the latter enables orthodontists to thoroughly monitor each patient, determine specific desired outcomes, track progress, and warn of potential changes in pre-existing pathology. However, there is limited evidence to assess the stability of treatment outcomes and relapse detection. The study concludes that AI is an effective tool for managing orthodontic treatment from diagnosis to retention, benefiting both patients and clinicians. Patients find the software easy to use and feel better cared for, while clinicians can make diagnoses more easily and assess compliance and damage to braces or aligners more quickly and frequently.
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18
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Sfondrini MF, Scribante A. New Materials and Techniques for Orthodontics. MATERIALS (BASEL, SWITZERLAND) 2023; 16:1924. [PMID: 36903039 PMCID: PMC10003920 DOI: 10.3390/ma16051924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Orthodontics is a specialty of dentistry dealing with the prevention, diagnosis, and treatment of mispositioned jaws and teeth [...].
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19
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Future of Orthodontics-A Systematic Review and Meta-Analysis on the Emerging Trends in This Field. J Clin Med 2023; 12:jcm12020532. [PMID: 36675459 PMCID: PMC9861462 DOI: 10.3390/jcm12020532] [Citation(s) in RCA: 4] [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: 12/24/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Technology is rapidly evolving in the modern world, and the accompanying developments due to its influence are shaping each and every aspect of our life, with the field of orthodontics being no exception. This systematic review and meta-analysis aimed to examine such trends in orthodontics and hypothesize which ones would emerge and continue in the near future. After a thorough search of online journals using keywords such as "3D printing," "Aligners," "Artificial intelligence," "Future trends," "Orthodontics," and "Teleorthodontics" across databases of PubMed-MEDLINE, Web of Science, Cochrane, and Scopus, a total of 634 papers were initially recovered. Technological advancements in 3D printing, Computer-aided design and Computer-aided manufacturing (CAD/CAM), biopolymers and Teleorthodontics were the most important categories of development seen across the 17 studies that we selected for our review. All the investigations selected for this systematic review depicted aspects of orthodontics that were influenced by rapid technological changes and could potentially become mainstream in the coming times. However, caution was sought to be observed in the usage/adoption of some of these trends, with social media usage amongst both patients as well as orthodontists being a prime example of this.
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Development, Application, and Performance of Artificial Intelligence in Cephalometric Landmark Identification and Diagnosis: A Systematic Review. Healthcare (Basel) 2022; 10:healthcare10122454. [PMID: 36553978 PMCID: PMC9778374 DOI: 10.3390/healthcare10122454] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/26/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
This study aimed to analyze the existing literature on how artificial intelligence is being used to support the identification of cephalometric landmarks. The systematic analysis of literature was carried out by performing an extensive search in PubMed/MEDLINE, Google Scholar, Cochrane, Scopus, and Science Direct databases. Articles published in the last ten years were selected after applying the inclusion and exclusion criteria. A total of 17 full-text articles were systematically appraised. The Cochrane Handbook for Systematic Reviews of Interventions (CHSRI) and Newcastle-Ottawa quality assessment scale (NOS) were adopted for quality analysis of the included studies. The artificial intelligence systems were mainly based on deep learning-based convolutional neural networks (CNNs) in the included studies. The majority of the studies proposed that AI-based automatic cephalometric analyses provide clinically acceptable diagnostic performance. They have worked remarkably well, with accuracy and precision similar to the trained orthodontist. Moreover, they can simplify cephalometric analysis and provide a quick outcome in practice. Therefore, they are of great benefit to orthodontists, as with these systems they can perform tasks more efficiently.
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21
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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022; 10:healthcare10071269. [PMID: 35885796 PMCID: PMC9320442 DOI: 10.3390/healthcare10071269] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
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Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12061359. [PMID: 35741169 PMCID: PMC9221941 DOI: 10.3390/diagnostics12061359] [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: 04/14/2022] [Revised: 05/21/2022] [Accepted: 05/27/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: The present study aims to evaluate and compare the model performances of different convolutional neural networks (CNNs) used for classifying sagittal skeletal patterns. (2) Methods: A total of 2432 lateral cephalometric radiographs were collected. They were labeled as Class I, Class II, and Class III patterns, according to their ANB angles and Wits values. The radiographs were randomly divided into the training, validation, and test sets in the ratio of 70%:15%:15%. Four different CNNs, namely VGG16, GoogLeNet, ResNet152, and DenseNet161, were trained, and their model performances were compared. (3) Results: The accuracy of the four CNNs was ranked as follows: DenseNet161 > ResNet152 > VGG16 > GoogLeNet. DenseNet161 had the highest accuracy, while GoogLeNet possessed the smallest model size and fastest inference speed. The CNNs showed better capabilities for identifying Class III patterns, followed by Classes II and I. Most of the samples that were misclassified by the CNNs were boundary cases. The activation area confirmed the CNNs without overfitting and indicated that artificial intelligence could recognize the compensatory dental features in the anterior region of the jaws and lips. (4) Conclusions: CNNs can quickly and effectively assist orthodontists in the diagnosis of sagittal skeletal classification patterns.
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Sycińska-Dziarnowska M, Szyszka-Sommerfeld L, Woźniak K, Lindauer SJ, Spagnuolo G. Predicting Interest in Orthodontic Aligners: A Google Trends Data Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:3105. [PMID: 35270796 PMCID: PMC8910220 DOI: 10.3390/ijerph19053105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 11/16/2022]
Abstract
Aligners are an example of how advances in dentistry can develop from innovative combinations of 3D technologies in imaging, planning and printing to provide new treatment modalities. With increasing demand for esthetic orthodontic treatment, aligners have grown in popularity because they are esthetically more pleasing and less obstructive to oral hygiene and other oral functions compared to fixed orthodontic appliances. To observe and estimate aligner treatment interest among Google Search users, Google Trends data were obtained and analyzed for the search term, "Invisalign". A prediction of interest for the year 2022 for three European Union countries with the highest GDP was developed. "Invisalign" was chosen to represent all orthodontic aligners as the most searched term in Google Trends for aligners. This is the first study to predict interest in the query "Invisalign" in a Google search engine. The Prophet algorithm, which depends on advanced statistical analysis methods, positions itself as an automatic prediction procedure and was used to predict Google Trends data. Seasonality modeling was based on the standard Fourier series to provide a flexible model of periodic effects. The results predict an increase in "Invisalign" in Google Trends queries in the coming year, increasing by around 6%, 9% and 13% by the end of 2022 compared to 2021 for France, Italy and Germany, respectively. Forecasting allows practitioners to plan for growing demand for particular treatments, consider taking continuing education, specifically, aligner certification courses, or introduce modern scanning technology into offices. The oral health community can use similar prediction tools and methods to remain alert to future changes in patient demand to improve the responses of professional organizations as a whole, work more effectively with governments if needed, and provide better coordination of care for patients.
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Affiliation(s)
- Magdalena Sycińska-Dziarnowska
- Department of Orthodontics, Pomeranian Medical University in Szczecin, Al. Powst. Wlkp. 72, 70111 Szczecin, Poland; (M.S.-D.); (L.S.-S.); (K.W.)
| | - Liliana Szyszka-Sommerfeld
- Department of Orthodontics, Pomeranian Medical University in Szczecin, Al. Powst. Wlkp. 72, 70111 Szczecin, Poland; (M.S.-D.); (L.S.-S.); (K.W.)
| | - Krzysztof Woźniak
- Department of Orthodontics, Pomeranian Medical University in Szczecin, Al. Powst. Wlkp. 72, 70111 Szczecin, Poland; (M.S.-D.); (L.S.-S.); (K.W.)
| | - Steven J. Lindauer
- Department of Orthodontics, School of Dentistry, Virginia Commonwealth University, Richmond, VA 23298, USA;
| | - Gianrico Spagnuolo
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Naples, Italy
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24
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Yamashiro T, Ko CC. Artificial intelligence and machine learning in orthodontics. Orthod Craniofac Res 2021; 24 Suppl 2:3-5. [PMID: 34825474 DOI: 10.1111/ocr.12543] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 10/18/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Takashi Yamashiro
- Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Osaka University, Suita, Japan
| | - Ching-Chang Ko
- Division of Orthodontics, The Ohio State University College of Dentistry, Columbus, Ohio, USA
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