Diagnosis of Tooth Prognosis Using Artificial Intelligence
The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/6/1422 |
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| author | Sang J. Lee Dahee Chung Akiko Asano Daisuke Sasaki Masahiko Maeno Yoshiki Ishida Takuya Kobayashi Yukinori Kuwajima John D. Da Silva Shigemi Nagai |
| author_facet | Sang J. Lee Dahee Chung Akiko Asano Daisuke Sasaki Masahiko Maeno Yoshiki Ishida Takuya Kobayashi Yukinori Kuwajima John D. Da Silva Shigemi Nagai |
| author_sort | Sang J. Lee |
| collection | DOAJ |
| description | The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan. |
| first_indexed | 2024-03-10T00:00:49Z |
| format | Article |
| id | doaj.art-dd30f8b5e08d4d3a937d1cf45a2ce5f4 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T00:00:49Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-dd30f8b5e08d4d3a937d1cf45a2ce5f42023-11-23T16:17:54ZengMDPI AGDiagnostics2075-44182022-06-01126142210.3390/diagnostics12061422Diagnosis of Tooth Prognosis Using Artificial IntelligenceSang J. Lee0Dahee Chung1Akiko Asano2Daisuke Sasaki3Masahiko Maeno4Yoshiki Ishida5Takuya Kobayashi6Yukinori Kuwajima7John D. Da Silva8Shigemi Nagai9Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, Boston, MA 02115, USAHarvard School of Dental Medicine, Boston, MA 02115, USADepartment of Restorative Dentistry, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, JapanDepartment of Periodontology, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, JapanDepartment of Adhesive Dentistry, School of Life Dentistry at Tokyo, The Nippon Dental University, Chiyoda-ku, Tokyo 102-8159, JapanDepartment of Dental Materials Science, School of Life Dentistry at Tokyo, The Nippon Dental University, Chiyoda-ku, Tokyo 102-8159, JapanDepartment of Oral Rehabilitation, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, JapanDepartment of Orthodontics, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, JapanDepartment of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, Boston, MA 02115, USADepartment of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA 02115, USAThe accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.https://www.mdpi.com/2075-4418/12/6/1422diagnosistooth prognosisartificial intelligence (AI)machine learningtreatment planprosthodontics |
| spellingShingle | Sang J. Lee Dahee Chung Akiko Asano Daisuke Sasaki Masahiko Maeno Yoshiki Ishida Takuya Kobayashi Yukinori Kuwajima John D. Da Silva Shigemi Nagai Diagnosis of Tooth Prognosis Using Artificial Intelligence Diagnostics diagnosis tooth prognosis artificial intelligence (AI) machine learning treatment plan prosthodontics |
| title | Diagnosis of Tooth Prognosis Using Artificial Intelligence |
| title_full | Diagnosis of Tooth Prognosis Using Artificial Intelligence |
| title_fullStr | Diagnosis of Tooth Prognosis Using Artificial Intelligence |
| title_full_unstemmed | Diagnosis of Tooth Prognosis Using Artificial Intelligence |
| title_short | Diagnosis of Tooth Prognosis Using Artificial Intelligence |
| title_sort | diagnosis of tooth prognosis using artificial intelligence |
| topic | diagnosis tooth prognosis artificial intelligence (AI) machine learning treatment plan prosthodontics |
| url | https://www.mdpi.com/2075-4418/12/6/1422 |
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