Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches
BackgroundThird molar extraction, a common dental procedure, often involves complications, such as alveolar nerve injury. Accurate preoperative assessment of the extraction difficulty and nerve injury risk is crucial for better surgical planning and patient outcomes. Recent advancements in deep lear...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Dental Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdmed.2025.1534406/full |
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| author | Hamza Al Salieti Hanan M. Qasem Sakhr Alshwayyat Sakhr Alshwayyat Sakhr Alshwayyat Noor Almasri Mustafa Alshwayyat Amira A. Aboali Farah Alsarayrah Lina Khasawneh Mohammed Al-mahdi Al-kurdi |
| author_facet | Hamza Al Salieti Hanan M. Qasem Sakhr Alshwayyat Sakhr Alshwayyat Sakhr Alshwayyat Noor Almasri Mustafa Alshwayyat Amira A. Aboali Farah Alsarayrah Lina Khasawneh Mohammed Al-mahdi Al-kurdi |
| author_sort | Hamza Al Salieti |
| collection | DOAJ |
| description | BackgroundThird molar extraction, a common dental procedure, often involves complications, such as alveolar nerve injury. Accurate preoperative assessment of the extraction difficulty and nerve injury risk is crucial for better surgical planning and patient outcomes. Recent advancements in deep learning (DL) have shown the potential to enhance the predictive accuracy using panoramic radiographic (PR) images. This systematic review evaluated the accuracy and reliability of DL models for predicting third molar extraction difficulty and inferior alveolar nerve (IAN) injury risk.MethodsA systematic search was conducted across PubMed, Scopus, Web of Science, and Embase until September 2024, focusing on studies assessing DL models for predicting extraction complexity and IAN injury using PR images. The inclusion criteria required studies to report predictive performance metrics. Study selection, data extraction, and quality assessment were independently performed by two authors using the PRISMA and QUADAS-2 guidelines.ResultsSix studies involving 12,419 PR images met the inclusion criteria. DL models demonstrated high accuracy in predicting extraction difficulty (up to 96%) and IAN injury (up to 92.9%), with notable sensitivity (up to 97.5%) for specific classifications, such as horizontal impactions. Geographically, three studies originated in South Korea and one each from Turkey and Thailand, limiting generalizability. Despite high accuracy, demographic data were sparsely reported, with only two studies providing patient sex distribution.ConclusionDL models show promise in improving the preoperative assessment of third molar extraction. However, further validation in diverse populations and integration with clinical workflows are necessary to establish its real-world utility, as limitations such as limited generalizability, potential selection bias and lack of long-term follow up remain challenges. |
| format | Article |
| id | doaj-art-ca81d86a689f4a28a8b2a06f00936433 |
| institution | DOAJ |
| issn | 2673-4915 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Dental Medicine |
| spelling | doaj-art-ca81d86a689f4a28a8b2a06f009364332025-08-20T03:07:27ZengFrontiers Media S.A.Frontiers in Dental Medicine2673-49152025-05-01610.3389/fdmed.2025.15344061534406Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approachesHamza Al Salieti0Hanan M. Qasem1Sakhr Alshwayyat2Sakhr Alshwayyat3Sakhr Alshwayyat4Noor Almasri5Mustafa Alshwayyat6Amira A. Aboali7Farah Alsarayrah8Lina Khasawneh9Mohammed Al-mahdi Al-kurdi10Faculty of Dentistry, Applied Science Private University, Amman, JordanFaculty of Dentistry, Jordan University of Science and Technology, Irbid, JordanResearch Associate, King Hussein Cancer Center, Amman, JordanInternship, Princess Basma Teaching Hospital, Irbid, JordanApplied Science Research Center, Applied Science Private University, Amman, JordanFaculty of Medicine, University of Jordan, Amman, JordanFaculty of Medicine, Jordan University of Science and Technology, Irbid, JordanDamanhour Teaching Hospital, General Organization for Teaching Hospitals and Institutes, Damanhour, EgyptFaculty of Dentistry, Jordan University of Science and Technology, Irbid, JordanDepartment of Prosthodontics, Faculty of Dentistry, Jordan University of Science and Technology, Irbid, Jordan0Faculty of Medicine, University of Aleppo, Aleppo, Syrian Arab RepublicBackgroundThird molar extraction, a common dental procedure, often involves complications, such as alveolar nerve injury. Accurate preoperative assessment of the extraction difficulty and nerve injury risk is crucial for better surgical planning and patient outcomes. Recent advancements in deep learning (DL) have shown the potential to enhance the predictive accuracy using panoramic radiographic (PR) images. This systematic review evaluated the accuracy and reliability of DL models for predicting third molar extraction difficulty and inferior alveolar nerve (IAN) injury risk.MethodsA systematic search was conducted across PubMed, Scopus, Web of Science, and Embase until September 2024, focusing on studies assessing DL models for predicting extraction complexity and IAN injury using PR images. The inclusion criteria required studies to report predictive performance metrics. Study selection, data extraction, and quality assessment were independently performed by two authors using the PRISMA and QUADAS-2 guidelines.ResultsSix studies involving 12,419 PR images met the inclusion criteria. DL models demonstrated high accuracy in predicting extraction difficulty (up to 96%) and IAN injury (up to 92.9%), with notable sensitivity (up to 97.5%) for specific classifications, such as horizontal impactions. Geographically, three studies originated in South Korea and one each from Turkey and Thailand, limiting generalizability. Despite high accuracy, demographic data were sparsely reported, with only two studies providing patient sex distribution.ConclusionDL models show promise in improving the preoperative assessment of third molar extraction. However, further validation in diverse populations and integration with clinical workflows are necessary to establish its real-world utility, as limitations such as limited generalizability, potential selection bias and lack of long-term follow up remain challenges.https://www.frontiersin.org/articles/10.3389/fdmed.2025.1534406/fullalveolar nervedeep learningmandibular nervepanoramic radiographicthird molar |
| spellingShingle | Hamza Al Salieti Hanan M. Qasem Sakhr Alshwayyat Sakhr Alshwayyat Sakhr Alshwayyat Noor Almasri Mustafa Alshwayyat Amira A. Aboali Farah Alsarayrah Lina Khasawneh Mohammed Al-mahdi Al-kurdi Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches Frontiers in Dental Medicine alveolar nerve deep learning mandibular nerve panoramic radiographic third molar |
| title | Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches |
| title_full | Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches |
| title_fullStr | Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches |
| title_full_unstemmed | Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches |
| title_short | Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches |
| title_sort | predicting alveolar nerve injury and the difficulty level of extraction impacted third molars a systematic review of deep learning approaches |
| topic | alveolar nerve deep learning mandibular nerve panoramic radiographic third molar |
| url | https://www.frontiersin.org/articles/10.3389/fdmed.2025.1534406/full |
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