Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery

IF 3.3 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Rita Moura , Dulce A. Oliveira , Marco P.L. Parente , Nina Kimmich , Luděk Hynčík , Lucie H. Hympánová , Renato M. Natal Jorge
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Abstract

Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making.

Abstract Image

预测阴道分娩过程中盆底动态的患者特异性替代模型
分娩是一项具有挑战性的事件,可能导致脱垂或尿失禁等长期后果。虽然计算模型被广泛用于模拟阴道分娩,但由于时间限制,这些模型与临床实践的结合受到阻碍。本研究的主要目的是引入一种人工智能管道,利用患者特异性替代模型来预测阴道分娩过程中的盆底损伤。本研究采用了一种基于有限元的机器学习方法,利用有限元模拟产生的信息生成数据集。我们进行了数千次分娩模拟,改变了盆底肌肉的尺寸和用于表征肌肉的机械性能。此外,还开发了一种网格变形算法,以获得针对特定患者的模型。对机器学习模型,特别是基于树的算法,如随机森林(RF)和极梯度提升,以及人工神经网络进行了训练,以预测骨盆底内节点的坐标,目的是预测关键间隔期间的肌肉拉伸。结果表明,射频模型表现最佳,平均绝对误差(MAE)为 0.086 毫米,平均绝对百分比误差为 0.38%。总体而言,超过 80% 的节点误差小于 0.1 毫米。计算拉伸的 MAE 等于 0.0011。这项工作证明了在临床实践中实施机器学习框架来预测潜在的孕产妇损伤并辅助医疗决策的可行性。
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来源期刊
Journal of the Mechanical Behavior of Biomedical Materials
Journal of the Mechanical Behavior of Biomedical Materials 工程技术-材料科学:生物材料
CiteScore
7.20
自引率
7.70%
发文量
505
审稿时长
46 days
期刊介绍: The Journal of the Mechanical Behavior of Biomedical Materials is concerned with the mechanical deformation, damage and failure under applied forces, of biological material (at the tissue, cellular and molecular levels) and of biomaterials, i.e. those materials which are designed to mimic or replace biological materials. The primary focus of the journal is the synthesis of materials science, biology, and medical and dental science. Reports of fundamental scientific investigations are welcome, as are articles concerned with the practical application of materials in medical devices. Both experimental and theoretical work is of interest; theoretical papers will normally include comparison of predictions with experimental data, though we recognize that this may not always be appropriate. The journal also publishes technical notes concerned with emerging experimental or theoretical techniques, letters to the editor and, by invitation, review articles and papers describing existing techniques for the benefit of an interdisciplinary readership.
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