Abstract：
OBJECTIVE: The prevalence of pelvic floor muscle injuries induced by childbirth is higher than 23 % in the general women population. Such injuries can lead to prolapses and other pathologies in future female life. Leveraging computational biomechanics, the study implements an advanced female pelvic floor model for computing the maximum pelvic muscle strain, which serves as an injury risk indicator. The design of experiment method, abbreviated as DoE, is used to compute the maximum strain for boundary values of bony pelvis dimensions, namely the anterior-posterior diameter (abbreviated as APD) and the transverse diameter (abbreviated as TD). This is done in combination with small, medium and large percentiles of fetal head circumference (abbreviated as HC).
METHODS: We utilized a previously developed finite element model of a female pelvic floor, as a reference, and enhanced it with new features, including a more detailed tissue geometry and advanced constitutive material models. The APD and TD dimensions were sourced from the set of MRI of 64 nulliparous women. This data was used to estimate the boundary dimensions of the female bony pelvis, combining both small and large values of APD and TD. Together with the 10th and the 95th percentiles for HC, a three-dimensional domain was constructed to assess the maximum pelvic muscle strain. In boundary cases, the maximum pelvic muscle strain was computed across 8 full-factorial design models (each situated at one corner of the domain, thereby combining the minimum and the maximum values of APD, TD and HC). This was done to define a response surface that predicts the maximum pelvic muscle strain within the domain. The accuracy of this response surface prediction was validated using 15 additional intermediate design models. These models were placed at the center of the domain (1 point), the centres of the domain boundary surfaces (6 points), and midway along each domain boundary edge (8 points).
RESULTS: The maximum strain results for 8 combinations of APD, TD, and HC were employed to construct a linear response surface as a function of APD, TD, and HC. Tests at an additional 19 domain points served to evaluate the efficiency of the response surface prediction. The response surface demonstrated strong predictability, with an absolute average error of 1.52 %, an absolute median error of 1.52 %, and an absolute maximum error of 11.11 %. HC emerged as the most influencing dimension, accounting for 16 % of influence.
CONCLUSIONS: The reference finite element pelvic floor model was scaled to 8 full-factorial female-specific pelvic floor models, which represent the combination of boundary values for APD, TD, and HC. The maximum pelvic floor muscle strain from these 8 models was used to design a response surface. When implementing the DoE approach to construct the response, there was consistent predictability for the maximum perineal muscle strain, as validated by the additional 19 intermediate design models. As a result, the response surface methodology can serve as an initial predictor for potential childbirth-induced pelvic floor muscle injury.
METHODS: We utilized a previously developed finite element model of a female pelvic floor, as a reference, and enhanced it with new features, including a more detailed tissue geometry and advanced constitutive material models. The APD and TD dimensions were sourced from the set of MRI of 64 nulliparous women. This data was used to estimate the boundary dimensions of the female bony pelvis, combining both small and large values of APD and TD. Together with the 10th and the 95th percentiles for HC, a three-dimensional domain was constructed to assess the maximum pelvic muscle strain. In boundary cases, the maximum pelvic muscle strain was computed across 8 full-factorial design models (each situated at one corner of the domain, thereby combining the minimum and the maximum values of APD, TD and HC). This was done to define a response surface that predicts the maximum pelvic muscle strain within the domain. The accuracy of this response surface prediction was validated using 15 additional intermediate design models. These models were placed at the center of the domain (1 point), the centres of the domain boundary surfaces (6 points), and midway along each domain boundary edge (8 points).
RESULTS: The maximum strain results for 8 combinations of APD, TD, and HC were employed to construct a linear response surface as a function of APD, TD, and HC. Tests at an additional 19 domain points served to evaluate the efficiency of the response surface prediction. The response surface demonstrated strong predictability, with an absolute average error of 1.52 %, an absolute median error of 1.52 %, and an absolute maximum error of 11.11 %. HC emerged as the most influencing dimension, accounting for 16 % of influence.
CONCLUSIONS: The reference finite element pelvic floor model was scaled to 8 full-factorial female-specific pelvic floor models, which represent the combination of boundary values for APD, TD, and HC. The maximum pelvic floor muscle strain from these 8 models was used to design a response surface. When implementing the DoE approach to construct the response, there was consistent predictability for the maximum perineal muscle strain, as validated by the additional 19 intermediate design models. As a result, the response surface methodology can serve as an initial predictor for potential childbirth-induced pelvic floor muscle injury.
摘要：
目的：在一般女性人群中，分娩诱发盆底肌损伤的患病率高于23%。这种伤害可能导致未来女性生活中的脱垂和其他病症。利用计算生物力学，该研究实施了一种先进的女性盆底模型，用于计算最大骨盆肌肉劳损，作为伤害风险指标。实验法的设计,缩写为DoE，用于计算骨骨盆尺寸边界值的最大应变，即前后直径（缩写为APD）和横向直径（缩写为TD）。这是结合小，胎儿头围的中大百分位数（缩写为HC）。
方法：我们利用了以前开发的女性盆底有限元模型，作为参考，并通过新功能增强了它，包括更详细的组织几何形状和先进的本构材料模型。APD和TD尺寸来自64名未产妇的MRI组。这些数据用于估计女性骨骨盆的边界尺寸，结合APD和TD的小值和大值。连同HC的第10百分位数和第95百分位数，我们构建了一个三维区域来评估最大骨盆肌肉劳损.在边界情况下，最大骨盆肌肉应变是在8个全因子设计模型(每个模型位于域的一个角，从而结合APD的最小值和最大值，TD和HC）。这样做是为了定义预测域内最大骨盆肌肉应变的响应面。使用15个额外的中间设计模型验证了该响应面预测的准确性。这些模型被放置在域的中心（1点），域边界面的中心（6点），以及沿每个域边界边缘的中途（8点）。
结果：8种APD组合的最大应变结果，TD,和HC被用来构建作为APD函数的线性响应面，TD,HC。另外19个域点的测试用于评估响应面预测的效率。响应面显示出很强的可预测性，绝对平均误差为1.52%，绝对中位数误差为1.52%，和11.11%的绝对最大误差。HC成为最具影响力的维度，占影响力的16%。
结论：将参考有限元盆底模型缩放为8个全因子女性特定的盆底模型，它们表示APD的边界值的组合，TD,HC。来自这8个模型的最大盆底肌肉应变用于设计响应面。当实施DoE方法来构建响应时，最大会阴肌肉拉伤有一致的可预测性，如额外的19个中间设计模型所验证的那样。因此,响应面法可以作为潜在的分娩引起的盆底肌肉损伤的初始预测指标.
方法：我们利用了以前开发的女性盆底有限元模型，作为参考，并通过新功能增强了它，包括更详细的组织几何形状和先进的本构材料模型。APD和TD尺寸来自64名未产妇的MRI组。这些数据用于估计女性骨骨盆的边界尺寸，结合APD和TD的小值和大值。连同HC的第10百分位数和第95百分位数，我们构建了一个三维区域来评估最大骨盆肌肉劳损.在边界情况下，最大骨盆肌肉应变是在8个全因子设计模型(每个模型位于域的一个角，从而结合APD的最小值和最大值，TD和HC）。这样做是为了定义预测域内最大骨盆肌肉应变的响应面。使用15个额外的中间设计模型验证了该响应面预测的准确性。这些模型被放置在域的中心（1点），域边界面的中心（6点），以及沿每个域边界边缘的中途（8点）。
结果：8种APD组合的最大应变结果，TD,和HC被用来构建作为APD函数的线性响应面，TD,HC。另外19个域点的测试用于评估响应面预测的效率。响应面显示出很强的可预测性，绝对平均误差为1.52%，绝对中位数误差为1.52%，和11.11%的绝对最大误差。HC成为最具影响力的维度，占影响力的16%。
结论：将参考有限元盆底模型缩放为8个全因子女性特定的盆底模型，它们表示APD的边界值的组合，TD,HC。来自这8个模型的最大盆底肌肉应变用于设计响应面。当实施DoE方法来构建响应时，最大会阴肌肉拉伤有一致的可预测性，如额外的19个中间设计模型所验证的那样。因此,响应面法可以作为潜在的分娩引起的盆底肌肉损伤的初始预测指标.
