Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review
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Published:2023-12-15
Issue:24
Volume:13
Page:3677
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ISSN:2075-4418
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Container-title:Diagnostics
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language:en
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Short-container-title:Diagnostics
Author:
Dipalma Gianna1ORCID, Inchingolo Alessio Danilo1ORCID, Inchingolo Angelo Michele1ORCID, Piras Fabio1ORCID, Carpentiere Vincenzo1ORCID, Garofoli Grazia1ORCID, Azzollini Daniela1ORCID, Campanelli Merigrazia1ORCID, Paduanelli Gregorio1, Palermo Andrea2ORCID, Inchingolo Francesco1ORCID
Affiliation:
1. Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy 2. Implant Dentistry College of Medicine and Dentistry Birmingham, University of Birmingham, Birmingham B46BN, UK
Abstract
This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions. Materials and Methods: Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated. Results: A final number of 33 studies were included in the review for qualitative analysis. Conclusions: These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.
Subject
Clinical Biochemistry
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