Artificial Intelligence in Periodontology: A Scoping Review
Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Dentistry Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6767/11/2/43 |
| _version_ | 1797621537511047168 |
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| author | James Scott Alberto M. Biancardi Oliver Jones David Andrew |
| author_facet | James Scott Alberto M. Biancardi Oliver Jones David Andrew |
| author_sort | James Scott |
| collection | DOAJ |
| description | Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn. |
| first_indexed | 2024-03-11T08:58:23Z |
| format | Article |
| id | doaj.art-e49832551474474b8abb617719d03513 |
| institution | Directory Open Access Journal |
| issn | 2304-6767 |
| language | English |
| last_indexed | 2024-03-11T08:58:23Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Dentistry Journal |
| spelling | doaj.art-e49832551474474b8abb617719d035132023-11-16T19:59:13ZengMDPI AGDentistry Journal2304-67672023-02-011124310.3390/dj11020043Artificial Intelligence in Periodontology: A Scoping ReviewJames Scott0Alberto M. Biancardi1Oliver Jones2David Andrew3School of Clinical Dentistry, The University of Sheffield, Claremont Crescent, Sheffield S10 2TA, UKDepartment of Infection, Immunity and Cardiovascular Disease, Polaris, 18 Claremont Crescent, Sheffield S10 2TA, UKSchool of Clinical Dentistry, The University of Sheffield, Claremont Crescent, Sheffield S10 2TA, UKSchool of Clinical Dentistry, The University of Sheffield, Claremont Crescent, Sheffield S10 2TA, UKArtificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn.https://www.mdpi.com/2304-6767/11/2/43periodontologyartificial intelligenceconvolutional neural networksradiography |
| spellingShingle | James Scott Alberto M. Biancardi Oliver Jones David Andrew Artificial Intelligence in Periodontology: A Scoping Review Dentistry Journal periodontology artificial intelligence convolutional neural networks radiography |
| title | Artificial Intelligence in Periodontology: A Scoping Review |
| title_full | Artificial Intelligence in Periodontology: A Scoping Review |
| title_fullStr | Artificial Intelligence in Periodontology: A Scoping Review |
| title_full_unstemmed | Artificial Intelligence in Periodontology: A Scoping Review |
| title_short | Artificial Intelligence in Periodontology: A Scoping Review |
| title_sort | artificial intelligence in periodontology a scoping review |
| topic | periodontology artificial intelligence convolutional neural networks radiography |
| url | https://www.mdpi.com/2304-6767/11/2/43 |
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