Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review
Background/purpose: In the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment pl...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Journal of Dental Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1991790220301161 |
| _version_ | 1818331901721575424 |
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| author | Sanjeev B. Khanagar Ali Al-Ehaideb Satish Vishwanathaiah Prabhadevi C. Maganur Shankargouda Patil Sachin Naik Hosam A. Baeshen Sachin S. Sarode |
| author_facet | Sanjeev B. Khanagar Ali Al-Ehaideb Satish Vishwanathaiah Prabhadevi C. Maganur Shankargouda Patil Sachin Naik Hosam A. Baeshen Sachin S. Sarode |
| author_sort | Sanjeev B. Khanagar |
| collection | DOAJ |
| description | Background/purpose: In the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment planning, and predicting the prognosis. Materials and methods: The literature for this paper was identified and selected by performing a thorough search for articles in the electronic data bases like Pubmed, Medline, Embase, Cochrane, and Google scholar, Scopus and Web of science, Saudi digital library published over the past two decades (January 2000–February 2020). After applying the inclusion and exclusion criteria, 16 articles were read in full and critically analyzed. QUADAS-2 were adapted for quality analysis of the studies included. Results: AI technology has been widely applied for identifying cephalometric landmarks, determining need for orthodontic extractions, determining the degree of maturation of the cervical vertebra, predicting the facial attractiveness after orthognathic surgery, predicting the need for orthodontic treatment, and orthodontic treatment planning. Most of these artificial intelligence models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). Conclusion: The results from these reported studies are suggesting that these automated systems have performed exceptionally well, with an accuracy and precision similar to the trained examiners. These systems can simplify the tasks and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently. These systems can be of great value in orthodontics. |
| first_indexed | 2024-12-13T13:27:13Z |
| format | Article |
| id | doaj.art-16e70d8335934314881dd7bd6bce92cf |
| institution | Directory Open Access Journal |
| issn | 1991-7902 |
| language | English |
| last_indexed | 2024-12-13T13:27:13Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Dental Sciences |
| spelling | doaj.art-16e70d8335934314881dd7bd6bce92cf2022-12-21T23:44:16ZengElsevierJournal of Dental Sciences1991-79022021-01-01161482492Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic reviewSanjeev B. Khanagar0Ali Al-Ehaideb1Satish Vishwanathaiah2Prabhadevi C. Maganur3Shankargouda Patil4Sachin Naik5Hosam A. Baeshen6Sachin S. Sarode7Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Corresponding author. Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Dental Services, King Abdulaziz Medical City- Ministry of National Guard Health Affairs, Riyadh, Saudi ArabiaDivision of Pedodontics, Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan, Saudi Arabia; Corresponding author. Division of Pedodontics, Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan, Saudi Arabia.Division of Pedodontics, Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan, Saudi ArabiaDepartment of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Saudi ArabiaDental Health Department, Dental Biomaterial Research Chair, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi ArabiaConsultant in Orthodontics, Department of Orthodontics, College of Dentistry, King Abdulaziz University, Riyadh, Saudi ArabiaDepartment of Oral and Maxillofacial Pathology, Dr. D.Y.Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, 411018, Maharashtra, IndiaBackground/purpose: In the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment planning, and predicting the prognosis. Materials and methods: The literature for this paper was identified and selected by performing a thorough search for articles in the electronic data bases like Pubmed, Medline, Embase, Cochrane, and Google scholar, Scopus and Web of science, Saudi digital library published over the past two decades (January 2000–February 2020). After applying the inclusion and exclusion criteria, 16 articles were read in full and critically analyzed. QUADAS-2 were adapted for quality analysis of the studies included. Results: AI technology has been widely applied for identifying cephalometric landmarks, determining need for orthodontic extractions, determining the degree of maturation of the cervical vertebra, predicting the facial attractiveness after orthognathic surgery, predicting the need for orthodontic treatment, and orthodontic treatment planning. Most of these artificial intelligence models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). Conclusion: The results from these reported studies are suggesting that these automated systems have performed exceptionally well, with an accuracy and precision similar to the trained examiners. These systems can simplify the tasks and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently. These systems can be of great value in orthodontics.http://www.sciencedirect.com/science/article/pii/S1991790220301161Artificial intelligenceAutomated orthodontic diagnosisDeep learningMachine learningArtificial neural networksConvolutional neural networks |
| spellingShingle | Sanjeev B. Khanagar Ali Al-Ehaideb Satish Vishwanathaiah Prabhadevi C. Maganur Shankargouda Patil Sachin Naik Hosam A. Baeshen Sachin S. Sarode Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review Journal of Dental Sciences Artificial intelligence Automated orthodontic diagnosis Deep learning Machine learning Artificial neural networks Convolutional neural networks |
| title | Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review |
| title_full | Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review |
| title_fullStr | Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review |
| title_full_unstemmed | Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review |
| title_short | Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review |
| title_sort | scope and performance of artificial intelligence technology in orthodontic diagnosis treatment planning and clinical decision making a systematic review |
| topic | Artificial intelligence Automated orthodontic diagnosis Deep learning Machine learning Artificial neural networks Convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1991790220301161 |
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