An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition
Dentists judge that the quality of dental treatment for each patient is very time-consuming and inefficient, lacks quantitative evaluation criteria, and is easy to cause errors. At the same time, the traditional method of extracting tooth and root canal image features based on experience is difficul...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2351714 |
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| author | Gang Peng Jie Liu Feng Yan Beicun Liu |
| author_facet | Gang Peng Jie Liu Feng Yan Beicun Liu |
| author_sort | Gang Peng |
| collection | DOAJ |
| description | Dentists judge that the quality of dental treatment for each patient is very time-consuming and inefficient, lacks quantitative evaluation criteria, and is easy to cause errors. At the same time, the traditional method of extracting tooth and root canal image features based on experience is difficult to accurately extract the tooth area and root canal filling area, resulting in low accuracy of tooth and root canal segmentation, which in turn affects the accuracy of tooth treatment quality evaluation. In this paper, a deep learning convolutional neural network is used to segment the root canal filling area, tooth boundary, and the boundary between tooth and soft tissue for the real patient ‘s root canal treatment and filling image. Finally, the segmented image is quantitatively evaluated according to the multi-evaluation index of professional doctors. The experimental results show that the intelligent evaluation method of dental treatment quality combined with deep learning and multi-index decomposition proposed in this paper not only unifies the evaluation criteria of dental treatment quality but also the therapeutic effect of quantitative scoring can effectively improve the work efficiency of doctors, which has reference significance for the application of artificial intelligence in the medical field. |
| format | Article |
| id | doaj-art-9ba2ee7fb7c44882929f1b68b5663180 |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-9ba2ee7fb7c44882929f1b68b56631802024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2351714An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index DecompositionGang Peng0Jie Liu1Feng Yan2Beicun Liu3School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaResearch Center, Hubei Eya Medical Investment Management Co., Ltd., Wuhan, ChinaResearch Center, Hubei Eya Medical Investment Management Co., Ltd., Wuhan, ChinaDentists judge that the quality of dental treatment for each patient is very time-consuming and inefficient, lacks quantitative evaluation criteria, and is easy to cause errors. At the same time, the traditional method of extracting tooth and root canal image features based on experience is difficult to accurately extract the tooth area and root canal filling area, resulting in low accuracy of tooth and root canal segmentation, which in turn affects the accuracy of tooth treatment quality evaluation. In this paper, a deep learning convolutional neural network is used to segment the root canal filling area, tooth boundary, and the boundary between tooth and soft tissue for the real patient ‘s root canal treatment and filling image. Finally, the segmented image is quantitatively evaluated according to the multi-evaluation index of professional doctors. The experimental results show that the intelligent evaluation method of dental treatment quality combined with deep learning and multi-index decomposition proposed in this paper not only unifies the evaluation criteria of dental treatment quality but also the therapeutic effect of quantitative scoring can effectively improve the work efficiency of doctors, which has reference significance for the application of artificial intelligence in the medical field.https://www.tandfonline.com/doi/10.1080/08839514.2024.2351714 |
| spellingShingle | Gang Peng Jie Liu Feng Yan Beicun Liu An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition Applied Artificial Intelligence |
| title | An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition |
| title_full | An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition |
| title_fullStr | An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition |
| title_full_unstemmed | An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition |
| title_short | An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition |
| title_sort | evaluation method of dental treatment quality combined with deep learning and multi index decomposition |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2351714 |
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