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机器学习预测阴道组织撕裂使用有限元模拟,由平面双轴测试告知

Mostafa Zakeri¹, Justin Krometis², Traian Iliescu³

¹Department of Mechanical Engineering, Virginia Tech, 325 Stanger Street, Blacksburg, VA, 24061, USA.

Annals of biomedical engineering

|March 13, 2026

概括

了解分娩撕裂机制至关重要. 这项研究使用有限元 (FE) 模拟和机器学习 (ML) 来预测组织应力和应变,发现撕裂方向是损伤风险评估的关键.

科学领域:

生物机械工程生物机械工程
计算建模计算建模
医疗器械模拟医疗器械模拟

背景情况:

与分娩相关的阴道撕裂是常见的,具有显著的长期影响.
了解驱动撕裂进展的机械因素受到体内研究困难的限制.
有限元素 (FE) 模型和机器学习 (ML) 为研究这些复杂的生物力学行为提供了潜在的解决方案.

研究的目的:

为了研究眼几何和纤维方向如何影响阴道组织中的局部应力和应变模式.
为了确定ML模型是否可以从FE模拟数据中准确预测组织反应.
开发一种用于评估分娩伤害风险的计算工具.

主要方法:

一个Holzapfel-Gasser-Ogden (HGO) 超弹性构成模型使用猪阴道组织数据进行校准.
对各种撕裂方向,大小和纤维对齐进行了FE模拟.
在FE生成的数据集上训练和验证了四个ML模型 (包括XGBoost).

主要成果:

FE模拟准确地复制了实验结果,验证了HGO模型.
极端梯度提升 (XGBoost) 证明了机械和几何输出的最高预测准确性.
眼方向被确定为影响眼边界局部压力和应变的最重要因素.

结论:

综合FE-ML方法在各种撕裂条件下提供了对阴道组织行为的快速和准确的预测.
这种方法绕过了使用合成数据进行额外的模拟或实验的需要.
该方法是伤害风险评估和产妇健康临床决策的宝贵工具.

关键词:

有限元素方法有限元素方法机器学习是机器学习.阴道撕裂的情况阴道组织的阴道组织.女性的健康妇女的健康

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