Advances in the application of deep learning to the risk assessment of nerve damage associated with extraction of impacted mandibular third molars
The application of deep learning (DL) has become widespread with the development of digital medicine. At present, DL has been gradually applied to the fields of stomatology. Multiple studies have applied DL, combined with preoperative examination images such as X ray and cone beam CT (CBCT) images,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Oral and Maxillofacial Surgery
2024-06-01
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| Series: | Kouqiang hemian waike zazhi |
| Subjects: | |
| Online Access: | https://journal06.magtech.org.cn/Jweb_joms/EN/10.12439/kqhm.1005-4979.2024.03.009 |
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| Summary: | The application of deep learning (DL) has become widespread with the development of digital medicine. At present, DL has been gradually applied to the fields of stomatology. Multiple studies have applied DL, combined with preoperative examination images such as X ray and cone beam CT (CBCT) images, to assist clinical diagnosis and decision-making in dealing with impacted mandibular third molar (IMTM). Besides, inferior alveolar nerve (IAN) injury is one of the most serious sequelae after extraction of IMTM. Combined with imageological examination, DL can provide objective and accurate estimation of the risk of IAN injury to improve the outcome of treatment. This paper reviews the current application of DL in preoperative image recognition, preoperative auxiliary diagnosis and evaluation, and IAN injury prognosis prediction in the extraction of IMTM, and looked into the role of DL in the extraction of IMTM in the future. |
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| ISSN: | 1005-4979 |