William Snyder , Mostafa Zakeri , Justin Krometis , Romesh Batra , Traian Iliescu , Raffaella De Vita
{"title":"Deep learning reduced order models of vaginal tear propagation","authors":"William Snyder , Mostafa Zakeri , Justin Krometis , Romesh Batra , Traian Iliescu , Raffaella De Vita","doi":"10.1016/j.jmbbm.2025.107074","DOIUrl":null,"url":null,"abstract":"<div><div>Childbirth often has traumatic consequences that profoundly affect the mother’s health. The passage of a baby through the vagina causes tissue lacerations, such as vaginal tears, which lead to pelvic floor disorders later in life. Despite advances in obstetrics, accurately predicting the possible complications of vaginal delivery remains challenging with current clinical methods. This paper introduces new computational methods that integrate finite element (FE) analysis, proper orthogonal decomposition (POD), and machine learning (ML) to predict vaginal deformations and tearing. Based on ex vivo micro-mechanical data collected from rodents, FE models of the vaginal canal subjected to increasing pressure with propagating tears are created. Snapshots of the FE displacement fields at increasing pressures and with different collagen fiber organization in the proximal, mid, and distal regions of the vagina are then used to develop (a) full-order ML models and (b) POD-based reduced order models with coefficients computed using ML. Both the full-order ML models and POD-ML models with POD bases of dimension <span><math><mrow><mi>l</mi><mo>≥</mo><mn>2</mn></mrow></math></span> approximated the FE results with root squared mean errors of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span>. Training (offline) times for the ML and POD-ML models were <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> and <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mn>10</mn><mo>)</mo></mrow></mrow></math></span> seconds, respectively, whereas prediction (online) times for both ML and POD-ML models were <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> seconds. Thus, the POD-ML models outperformed the ML models in terms of training efficiency while achieving similar prediction accuracy. Our findings demonstrate that the integration of these techniques can lead to faster computations of vaginal delivery outcomes. POD-based reduced order models and ML-based computational tools emerge as non-invasive methods for quantifying vaginal tissue deformations and tears.</div></div>","PeriodicalId":380,"journal":{"name":"Journal of the Mechanical Behavior of Biomedical Materials","volume":"170 ","pages":"Article 107074"},"PeriodicalIF":3.5000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Mechanical Behavior of Biomedical Materials","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1751616125001900","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Childbirth often has traumatic consequences that profoundly affect the mother’s health. The passage of a baby through the vagina causes tissue lacerations, such as vaginal tears, which lead to pelvic floor disorders later in life. Despite advances in obstetrics, accurately predicting the possible complications of vaginal delivery remains challenging with current clinical methods. This paper introduces new computational methods that integrate finite element (FE) analysis, proper orthogonal decomposition (POD), and machine learning (ML) to predict vaginal deformations and tearing. Based on ex vivo micro-mechanical data collected from rodents, FE models of the vaginal canal subjected to increasing pressure with propagating tears are created. Snapshots of the FE displacement fields at increasing pressures and with different collagen fiber organization in the proximal, mid, and distal regions of the vagina are then used to develop (a) full-order ML models and (b) POD-based reduced order models with coefficients computed using ML. Both the full-order ML models and POD-ML models with POD bases of dimension approximated the FE results with root squared mean errors of . Training (offline) times for the ML and POD-ML models were and seconds, respectively, whereas prediction (online) times for both ML and POD-ML models were seconds. Thus, the POD-ML models outperformed the ML models in terms of training efficiency while achieving similar prediction accuracy. Our findings demonstrate that the integration of these techniques can lead to faster computations of vaginal delivery outcomes. POD-based reduced order models and ML-based computational tools emerge as non-invasive methods for quantifying vaginal tissue deformations and tears.
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.