Abstract：
OBJECTIVE: The aim of this study was to evaluate the accuracy of a combined approach based on an isotopological remeshing and statistical shape analysis (SSA) to capture key anatomical features of altered and intact premolars. Additionally, the study compares the capabilities of four Machine Learning (ML) algorithms in identifying or simulating tooth alterations.
METHODS: 113 premolar surfaces from a multicenter database were analyzed. These surfaces were processed using an isotopological remeshing method, followed by a SSA. Mean Euclidean distances between the initial and remeshed STL files were calculated to assess deviation in anatomical landmark positioning. Seven anatomical features were extracted from each tooth, and their correlations with shape modes and morphological characteristics were explored. Four ML algorithms, validated through three-fold cross-validation, were assessed for their ability to classify tooth types and alterations. Additionally, twenty intact teeth were altered and then reconstructed to verify the method\'s accuracy.
RESULTS: The first five modes encapsulated 76.1% of the total shape variability, with a mean landmark positioning deviation of 10.4 µm (±6.4). Significant correlations were found between shape modes and specific morphological features. The optimal ML algorithms demonstrated high accuracy (>83%) and precision (>86%). Simulations on intact teeth showed discrepancies in anatomical features below 3%.
CONCLUSIONS: The combination of an isotopological remeshing with SSA showed good reliability in capturing key anatomical features of the tooth.
CONCLUSIONS: The encouraging performance of ML algorithms suggests a promising direction for supporting practitioners in diagnosing and planning treatments for patients with altered teeth, ultimately improving preventive care.
METHODS: 113 premolar surfaces from a multicenter database were analyzed. These surfaces were processed using an isotopological remeshing method, followed by a SSA. Mean Euclidean distances between the initial and remeshed STL files were calculated to assess deviation in anatomical landmark positioning. Seven anatomical features were extracted from each tooth, and their correlations with shape modes and morphological characteristics were explored. Four ML algorithms, validated through three-fold cross-validation, were assessed for their ability to classify tooth types and alterations. Additionally, twenty intact teeth were altered and then reconstructed to verify the method\'s accuracy.
RESULTS: The first five modes encapsulated 76.1% of the total shape variability, with a mean landmark positioning deviation of 10.4 µm (±6.4). Significant correlations were found between shape modes and specific morphological features. The optimal ML algorithms demonstrated high accuracy (>83%) and precision (>86%). Simulations on intact teeth showed discrepancies in anatomical features below 3%.
CONCLUSIONS: The combination of an isotopological remeshing with SSA showed good reliability in capturing key anatomical features of the tooth.
CONCLUSIONS: The encouraging performance of ML algorithms suggests a promising direction for supporting practitioners in diagnosing and planning treatments for patients with altered teeth, ultimately improving preventive care.
摘要：
目的：这项研究的目的是评估基于同位素重新网格和统计形状分析（SSA）的组合方法的准确性，以捕获改变和完整的前磨牙的关键解剖特征。此外,该研究比较了四种机器学习（ML）算法在识别或模拟牙齿改变方面的能力。
方法：分析了多中心数据库中的113个前磨牙表面。这些表面使用同位素重新划分方法进行处理，其次是SSA。计算初始和重新网格的STL文件之间的平均欧几里德距离，以评估解剖标志定位的偏差。从每颗牙齿中提取了七个解剖特征，并探讨了它们与形态模式和形态特征的相关性。四种ML算法，通过三次交叉验证进行验证，评估了他们对牙齿类型和改变进行分类的能力。此外,对20颗完整的牙齿进行了改造，然后进行了重建，以验证该方法的准确性。
结果：前五种模式封装了76.1％的总形状变异性，平均地标定位偏差为10.4µm(±6.4)。在形状模式和特定的形态特征之间发现了显着的相关性。最佳ML算法显示出较高的准确性（>83％）和精度（>86％）。对完整牙齿的模拟显示，解剖特征的差异低于3％。
结论：同位素重新啮合与SSA的组合在捕获牙齿的关键解剖特征方面显示出良好的可靠性。
结论：ML算法的令人鼓舞的表现为支持从业者诊断和规划牙齿改变患者的治疗提供了一个有希望的方向。最终改善预防性护理。
方法：分析了多中心数据库中的113个前磨牙表面。这些表面使用同位素重新划分方法进行处理，其次是SSA。计算初始和重新网格的STL文件之间的平均欧几里德距离，以评估解剖标志定位的偏差。从每颗牙齿中提取了七个解剖特征，并探讨了它们与形态模式和形态特征的相关性。四种ML算法，通过三次交叉验证进行验证，评估了他们对牙齿类型和改变进行分类的能力。此外,对20颗完整的牙齿进行了改造，然后进行了重建，以验证该方法的准确性。
结果：前五种模式封装了76.1％的总形状变异性，平均地标定位偏差为10.4µm(±6.4)。在形状模式和特定的形态特征之间发现了显着的相关性。最佳ML算法显示出较高的准确性（>83％）和精度（>86％）。对完整牙齿的模拟显示，解剖特征的差异低于3％。
结论：同位素重新啮合与SSA的组合在捕获牙齿的关键解剖特征方面显示出良好的可靠性。
结论：ML算法的令人鼓舞的表现为支持从业者诊断和规划牙齿改变患者的治疗提供了一个有希望的方向。最终改善预防性护理。
