Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography
Abstract Background External root resorption (ERR) during orthodontic treatment is a common concern, and accurate quantification is crucial for assessing outcomes and minimizing long-term complications. This study aims to quantify ERR using automated root extraction from cone beam computed tomograph...
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BMC
2025-03-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-05706-y |
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| author | Jing Huang Mengjie Wang Xuan Tang Leilei Zheng Chongshi Yang |
| author_facet | Jing Huang Mengjie Wang Xuan Tang Leilei Zheng Chongshi Yang |
| author_sort | Jing Huang |
| collection | DOAJ |
| description | Abstract Background External root resorption (ERR) during orthodontic treatment is a common concern, and accurate quantification is crucial for assessing outcomes and minimizing long-term complications. This study aims to quantify ERR using automated root extraction from cone beam computed tomography (CBCT), combined with a novel root partitioning method and enhanced through the integration of intraoral scans for improved accuracy. Methods Thirty-six patients with malocclusion were included and divided into two groups. Root extraction was performed on CBCT images using artificial intelligence (AI), Simultaneously, crown data from intraoral scans were integrated to create composite dental models in Geomagic software. Pre- and post-treatment models were aligned based on crowns. A novel partitioning method was then used to analyze root volume changes in three dimensions. Finally, these changes were analyzed according to age, tooth region, and extraction treatment using SPSS software. Results A statistically significant reduction in root volume was observed post-treatment in both groups (P < 0.001). Anterior teeth exhibited greater ERR, especially in the upper anterior teeth of Group II (extraction treatment, P < 0.05). Posterior maxillary teeth in Group I showed less ERR (P < 0.05). ERR was more pronounced in the apical third of the root (P < 0.001). Group II experienced greater overall ERR, particularly in the apical third, whereas Group I showed more ERR at the cervical third (P < 0.05). Conclusion This 3D quantification method provides a novel assessment of ERR, with distribution influenced by age, tooth position, extraction treatment, and root region. |
| format | Article |
| id | doaj-art-fe69cbb535414c688e1d04db3313d5a0 |
| institution | OA Journals |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-fe69cbb535414c688e1d04db3313d5a02025-08-20T01:57:48ZengBMCBMC Oral Health1472-68312025-03-012511910.1186/s12903-025-05706-yThree-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomographyJing Huang0Mengjie Wang1Xuan Tang2Leilei Zheng3Chongshi Yang4College of Stomatology, Chongqing Medical UniversityCollege of Stomatology, Chongqing Medical UniversityCollege of Stomatology, Chongqing Medical UniversityCollege of Stomatology, Chongqing Medical UniversityCollege of Stomatology, Chongqing Medical UniversityAbstract Background External root resorption (ERR) during orthodontic treatment is a common concern, and accurate quantification is crucial for assessing outcomes and minimizing long-term complications. This study aims to quantify ERR using automated root extraction from cone beam computed tomography (CBCT), combined with a novel root partitioning method and enhanced through the integration of intraoral scans for improved accuracy. Methods Thirty-six patients with malocclusion were included and divided into two groups. Root extraction was performed on CBCT images using artificial intelligence (AI), Simultaneously, crown data from intraoral scans were integrated to create composite dental models in Geomagic software. Pre- and post-treatment models were aligned based on crowns. A novel partitioning method was then used to analyze root volume changes in three dimensions. Finally, these changes were analyzed according to age, tooth region, and extraction treatment using SPSS software. Results A statistically significant reduction in root volume was observed post-treatment in both groups (P < 0.001). Anterior teeth exhibited greater ERR, especially in the upper anterior teeth of Group II (extraction treatment, P < 0.05). Posterior maxillary teeth in Group I showed less ERR (P < 0.05). ERR was more pronounced in the apical third of the root (P < 0.001). Group II experienced greater overall ERR, particularly in the apical third, whereas Group I showed more ERR at the cervical third (P < 0.05). Conclusion This 3D quantification method provides a novel assessment of ERR, with distribution influenced by age, tooth position, extraction treatment, and root region.https://doi.org/10.1186/s12903-025-05706-yAutomatic root extractionCone-beam computed tomographyExternal root resorptionQuantitationThree-dimensional imaging |
| spellingShingle | Jing Huang Mengjie Wang Xuan Tang Leilei Zheng Chongshi Yang Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography BMC Oral Health Automatic root extraction Cone-beam computed tomography External root resorption Quantitation Three-dimensional imaging |
| title | Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography |
| title_full | Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography |
| title_fullStr | Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography |
| title_full_unstemmed | Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography |
| title_short | Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography |
| title_sort | three dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone beam computed tomography |
| topic | Automatic root extraction Cone-beam computed tomography External root resorption Quantitation Three-dimensional imaging |
| url | https://doi.org/10.1186/s12903-025-05706-y |
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