BMC oral health · 2025
Three-dimensional partitioning and quantification of orthodontic root resorption via automatic root extraction from cone-beam computed tomography.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Huang Jing, Wang Mengjie, Tang Xuan, Zheng Leilei, Yang Chongshi
Аннотация
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.
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.