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Purpose <p>Constipation is a prevalent disorder of the digestive system, and abdominal massage is commonly employed to alleviate its symptoms due to its non-invasive nature and minimal side effects. This study aims to develop an accurate correlation model that links massage force (MF), massage depth (MD), and intra-abdominal wall deformation (IAWD) based on patient-specific data, thereby providing quantitative standards and personalized treatment options for the use of clinical massage devices.</p> Methods <p>The study utilized finite element method (FEM) and machine learning (ML) techniques to construct a digital twin model of the abdomen. Initially, a high-fidelity skin-muscle two-layer abdominal finite element model was developed using computed tomography (CT) images and mechanical experimental data. Subsequently, four individual learning models were trained, and stacked models were constructed by combining the advantages of these individual models through random forest and K-nearest neighbor algorithms to predict MF and IAWD.</p> Results <p>The prediction accuracy rates for MF in the ascending colon, transverse colon, and descending colon regions were 84.80%, 84.00%, and 85.60%, respectively. The prediction accuracy rates for IAWD were 96.00%, 87.00%, and 95.20%, respectively.</p> Conclusion <p>Compared to traditional finite element simulations, this abdominal digital twin model is capable of delivering predictions within 3 seconds, offering valuable insights for the digital transformation of healthcare.</p>

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Prediction of Massage Force and Intra-abdominal Wall Deformation During Massage by a Digital Twin Model Based on an Abdominal Finite Element Model

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Junjie Fu,
  • Yanchen Du,
  • Xinyi Tang,
  • Hongliu Yu

摘要

Purpose

Constipation is a prevalent disorder of the digestive system, and abdominal massage is commonly employed to alleviate its symptoms due to its non-invasive nature and minimal side effects. This study aims to develop an accurate correlation model that links massage force (MF), massage depth (MD), and intra-abdominal wall deformation (IAWD) based on patient-specific data, thereby providing quantitative standards and personalized treatment options for the use of clinical massage devices.

Methods

The study utilized finite element method (FEM) and machine learning (ML) techniques to construct a digital twin model of the abdomen. Initially, a high-fidelity skin-muscle two-layer abdominal finite element model was developed using computed tomography (CT) images and mechanical experimental data. Subsequently, four individual learning models were trained, and stacked models were constructed by combining the advantages of these individual models through random forest and K-nearest neighbor algorithms to predict MF and IAWD.

Results

The prediction accuracy rates for MF in the ascending colon, transverse colon, and descending colon regions were 84.80%, 84.00%, and 85.60%, respectively. The prediction accuracy rates for IAWD were 96.00%, 87.00%, and 95.20%, respectively.

Conclusion

Compared to traditional finite element simulations, this abdominal digital twin model is capable of delivering predictions within 3 seconds, offering valuable insights for the digital transformation of healthcare.