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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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Amendolara A, Pfister D, Settelmayer M, Shah M, Wu V, Donnelly S, Johnston B, Peterson R, Sant D, Kriak J, Bills K. An Overview of Machine Learning Applications in Sports Injury Prediction. Cureus 2023; 15:e46170. [PMID: 37905265 PMCID: PMC10613321 DOI: 10.7759/cureus.46170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 11/02/2023] Open
Abstract
Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.
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Affiliation(s)
- Alfred Amendolara
- Federated Department of Biology, New Jersey Institute of Technology, Newark, USA
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Devin Pfister
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Marina Settelmayer
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Mujtaba Shah
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Veronica Wu
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Sean Donnelly
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Brooke Johnston
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Race Peterson
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - David Sant
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - John Kriak
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
| | - Kyle Bills
- Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, USA
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Intelligence and Neuroscience C. Retracted: Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:9759841. [PMID: 37564535 PMCID: PMC10412211 DOI: 10.1155/2023/9759841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 08/12/2023]
Abstract
[This retracts the article DOI: 10.1155/2021/3056428.].
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Tang Y, Zan S, Zhang X. Research on System Construction and Strategy of Intelligent Sports in the Implementation of National Fitness. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3190801. [PMID: 35592719 PMCID: PMC9113877 DOI: 10.1155/2022/3190801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022]
Abstract
This paper studies the construction and development strategy of intelligent sports system in the context of Chinese National Fitness Program with methods of literature review and model construction. The research shows that there are four dilemmas in the implementation of intelligent sports in national fitness: data security, market monopoly, legal supervision, and product iteration. However, there are also three promoting factors in this regard, including policy guarantee, market demand, and industrial upgrading. Following the principles of scientificity, effectiveness, public welfare, and collaboration, this paper designs a system for intelligent sports in national fitness. The construction of the national fitness intelligent sports system mainly consists of four modules, including basic framework construction, function design, content design, and operation analysis. With the systematic analysis of the status quo of intelligent sports application in national fitness, this paper puts forward intelligent sports development strategies in the implementation of national fitness from four aspects: optimizing the top-level design of government, speeding up industrial transformation and upgrading, constructing market supervision mechanism, and establishing a talent training system.
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Affiliation(s)
- Yuxin Tang
- School of Physical Education, Shandong University, Jinan, Shandong 250061, China
| | - Shengfeng Zan
- School of Physical Education, Shandong University, Jinan, Shandong 250061, China
| | - Xiaowen Zhang
- School of International Studies, Renmin University of China, Beijing 100872, China
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