Machine learning and orthodontics, current trends and the future opportunities: A scoping review

Mohammad-Rahimi, H ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1016/j.ajodo.2021.02.013
  3. Publisher: Mosby Inc , 2021
  4. Abstract:
  5. Introduction: In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology show promising results in the practice of orthodontics. This scoping review aimed to investigate the effectiveness of AI-based models employed in orthodontic landmark detection, diagnosis, and treatment planning. Methods: A precise search of electronic databases was conducted, including PubMed, Google Scholar, Scopus, and Embase (English publications from January 2010 to July 2020). Quality Assessment and Diagnostic Accuracy Tool 2 (QUADAS-2) was used to assess the quality of the articles included in this review. Results: After applying inclusion and exclusion criteria, 49 articles were included in the final review. AI technology has achieved state-of-the-art results in various orthodontic applications, including automated landmark detection on lateral cephalograms and photography images, cervical vertebra maturation degree determination, skeletal classification, orthodontic tooth extraction decisions, predicting the need for orthodontic treatment or orthognathic surgery, and facial attractiveness. Most of the AI models used in these applications are based on artificial neural networks. Conclusions: AI can help orthodontists save time and provide accuracy comparable to the trained dentists in diagnostic assessments and prognostic predictions. These systems aim to boost performance and enhance the quality of care in orthodontics. However, based on current studies, the most promising application was cephalometry landmark detection, skeletal classification, and decision making on tooth extractions. © 2021 American Association of Orthodontists
  6. Keywords:
  7. Artificial intelligence ; Artificial neural network ; Cervical vertebra ; Decision making ; Diagnostic accuracy ; Diagnostic test accuracy study ; Embase ; Face ; Human ; Medline ; Orthodontic procedure ; Orthodontist ; Orthognathic surgery ; Photography ; Prediction ; Quality control ; Review ; Scopus ; Search engine ; Systematic review ; Tooth extraction ; Machine learning ; Cephalometry ; Humans ; Neural Networks, Computer ; Orthodontics
  8. Source: American Journal of Orthodontics and Dentofacial Orthopedics ; Volume 160, Issue 2 , 2021 , Pages 170-192.e4 ; 08895406 (ISSN)
  9. URL: https://linkinghub.elsevier.com/retrieve/pii/S0889540621002419