目的:利用T-Scan定量分析单颗后牙种植修复体在牙尖交错位(intercuspal position,ICP)时咬合接触情况,并探讨其对种植体周围垂直骨吸收的影响。方法:选取第一磨牙常规种植修复的40例无咬合高点等不良修复现象和磨牙症等不良习惯的患者。...目的:利用T-Scan定量分析单颗后牙种植修复体在牙尖交错位(intercuspal position,ICP)时咬合接触情况,并探讨其对种植体周围垂直骨吸收的影响。方法:选取第一磨牙常规种植修复的40例无咬合高点等不良修复现象和磨牙症等不良习惯的患者。T-Scan记录种植修复体及对侧同名牙咬合接触情况的差异,分别按照咬合延迟时间、牙合力百分率比、咬合力峰值及咬合接触面积比进行分组,用影像学测量技术评估种植体周围骨组织的变化,从而分析咬合因素对种植体周围垂直骨吸收(vertical bone loss,VBL)的影响,并建立多元线性回归模型。结果:在咬合延迟时间和咬合力峰值分组比较中,平均VBL均存在显著性差异(P<0.05)。同时得出预测方程:Y=1.060-0.734X1+0.248X2-0.589X3,其中Y为平均VBL、X1为咬合延迟时间、X2为咬合力峰值、X3为咬合接触面积比。结论:在单颗后牙缺失常规种植修复时除考虑适当的咬合设计外,在调整种植修复体的咬合接触或定期咬合检查时,推荐应用T-Scan进行定量分析以确保ICP时咬合力峰值尽可能降低(即轻咬合接触),和适当延迟种植修复体首次咬合接触时间,对维持种植体周围骨的稳定性极为有益。展开更多
In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or ove...In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.展开更多
文摘目的:利用T-Scan定量分析单颗后牙种植修复体在牙尖交错位(intercuspal position,ICP)时咬合接触情况,并探讨其对种植体周围垂直骨吸收的影响。方法:选取第一磨牙常规种植修复的40例无咬合高点等不良修复现象和磨牙症等不良习惯的患者。T-Scan记录种植修复体及对侧同名牙咬合接触情况的差异,分别按照咬合延迟时间、牙合力百分率比、咬合力峰值及咬合接触面积比进行分组,用影像学测量技术评估种植体周围骨组织的变化,从而分析咬合因素对种植体周围垂直骨吸收(vertical bone loss,VBL)的影响,并建立多元线性回归模型。结果:在咬合延迟时间和咬合力峰值分组比较中,平均VBL均存在显著性差异(P<0.05)。同时得出预测方程:Y=1.060-0.734X1+0.248X2-0.589X3,其中Y为平均VBL、X1为咬合延迟时间、X2为咬合力峰值、X3为咬合接触面积比。结论:在单颗后牙缺失常规种植修复时除考虑适当的咬合设计外,在调整种植修复体的咬合接触或定期咬合检查时,推荐应用T-Scan进行定量分析以确保ICP时咬合力峰值尽可能降低(即轻咬合接触),和适当延迟种植修复体首次咬合接触时间,对维持种植体周围骨的稳定性极为有益。
文摘In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.
