Förlossningsbristningar involverar olika former av skador på vagina, vulva, perineum och analsfinktrar. Skadan kan uppstå spontant under födseln eller genom iatrogena skador som hälso- och sjukvården åstadkommit. Bristningar uppskattas drabba cirka 80 procent av förstföderskor och kan leda till komplikationer som kan påverka livskvaliteten på flera sätt. Genom historien har vårdandet av bäckenbottens vävnader studerats, men allteftersom barnafödseln förflyttades till sjukhus och förlossningsvården medikaliserades kan en stor del av förlossningskunskapen ha försvunnit. Födslorummet blev till ett vårdrum och förlossningssfären till ett kliniskt skeende. Detta kan i sin tur ha bidragit till att förlossningsbristningar kommit att normaliseras. Syftet var att kartlägga hur olika metoder kan förebygga uppkomsten av förlossningsbristningar. Vald metod blev en icke-systematisk litteraturöversikt som omfattade randomiserade kontrollerade studier, observationsstudier, tvärsnittsstudier samt kohortstudier med kvantitativ design. Databassökningen utfördes i PubMed och CINAHL. En kvalitetsgranskning utfördes på samtliga artiklar och endast ett urval som uppfyllde grundläggande kvalitetskrav ingick i litteraturöversikten. Med hjälp av en integrerad analys kunde en sammanvägning av flera studier genomföras och sammanställas till ett gemensamt resultat. Kvantitativa data extraherades från artiklarnas resultat och omformulerades till beskrivande text. Totalt 31 artiklar med kvantitativ ansats identifierades som svarade an på syftet och blev grunden för den icke-systematiska litteraturöversikten. Genom dataanalysen identifierades flera metoder som kan förebygga uppkomsten av förlossningsbristningar. Samtliga metoder delades in under två huvudkategorier: ”Metoder med god skyddande effekt på förlossningsbristningar” samt ”Metoder med tveksamt skyddande effekt på förlossningsbristningar”, med sju tillhörande underkategorier: ”Vårdande av perineum under graviditeten”, ”Vårdande av perineum i samband med förlossningen”, ”Barnmorskeledd förlossning”, ”Ett kontrollerat framfödande”, ”Barnmorskans händer och kommunikation som verktyg för att motverka förlossningsbristningar”, ”förlossningspositioner och att föda i vatten” samt ”Perineotomi i förebyggande syfte mot förlossningsbristningar”. Slutsatsen indikerade på flera metoder som kan motverka uppkomsten av förlossningsbristningar. De metoder som visade på god skyddande effekt var värme och perinealmassage, barnmorskeledda förlossningar och närvaron av två barnmorskor vid framfödandet samt att föda i sidoläge och fyrfota position. Metoder som visade på tveksamt skyddande effekt relaterat till tvetydiga resultat var att föda i litotomiläge, rutinmässig användning av perineotomi samt hands on-tekniken. På grund av osäker evidens ökar komplexiteten för forskningsområdet. / Vaginal tearing involves various forms of damage to the vagina, vulva, perineum and anal sphincters. Vaginal tearing can occur spontaneously during birth or through iatrogenic injuries achieved by the healthcare providers. Vaginal tearing is estimated to affect about 80 percent of primiparous women and can lead to complications that can affect the quality of life in several ways. Throughout history, care of the pelvic floor tissues has been studied, but as maternity care has become more medicalized, pregnancy and childbirth came to be seen as a pathological event. This, in turn, may have contributed towards the normalization of vaginal tearing. The aim was to examine how different methods can prevent the occurrence of vaginal tearing during childbirth. The method chosen was a scoping review that included the analysis of randomized controlled trials, observational studies, cross-sectional studies and cohort studies utilizing a quantitative approach. The database search was performed in PubMed and CINAHL. A quality review was performed on all articles and only the selection that met the quality requirements was included. By using an integrated analysis several studies compiled to produce a conclusive result. Quantitative data was extracted from the results of the articles and reformulated into a descriptive text. A total of 31 articles with a quantitative approach were identified that answered this thesis’ aim and built the structure of the scoping review. Through the data analysis, two main categories with associated sub-categories were identified. The main categories that have emerged are: "Methods with good protective effect on vaginal tearing" and "Methods with questionable protective effect on vaginal tearing". Sub-categories that have been identified are: "Perineal care during pregnancy", "Perineal care during birth”, "Midwife-led birth", “Controlled vaginal delivery”, "Midwife's hands and communication to prevent vaginal tearing”, "Delivery positions and water birth" and "Episiotomy as a preventive measure against vaginal tearing ". The conclusions of this thesis’ research indicated several methods that could prevent vaginal tearing. In particular, there was good evidence to establish that caring for the perineal area by massaging the perineum and applying warm compresses, midwife-led birth and the presence of two midwifes during birth as well as birth in lateral position and hands-and-knees position. Methods that were found to be ineffective included birth in litothomy position, routine practice of episiotomy as well as the hands on-technique. Due to inconclusive evidence the complexity of the research area increases.

High-fidelity computer simulations of childbirth are time consuming, making them impractical for guiding decision-making during obstetric emergencies. The complex geometry, micro-structure, and large finite deformations undergone by the vagina during childbirth result in material and geometric nonlinearities, complicated boundary conditions, and nonhomogeneities within finite element (FE) simulations. Such nonlinearities pose a significant challenge for numerical solvers, increasing the computational time. Simplifying assumptions can reduce the computational time significantly, but this usually comes at the expense of simulation accuracy. The work herein proposed the use of reduced order modeling (ROM) techniques to create surrogate models that capture experimentally-measured displacement fields of rat vaginal tissue during inflation testing in order to attain both the accuracy of higher-fidelity models and the speed of lower-fidelity simulations. The proper orthogonal decomposition (POD) method was used to extract the significant information from FE simulations generated by varying the luminal pressure and the parameters that introduce the anisotropy in the selected constitutive model. In our first study, a new data-driven (DD) variational multiscale (VMS) ROM framework was extended to obtain the displacement fields of rat vaginal tissue subjected to ramping luminal pressure. For comparison purposes, we also investigated the classical Galerkin ROM (G-ROM). In our numerical study, both the G-ROM and the DD-VMS-ROM decreased the FE computational cost by orders of magnitude without a significant decrease in numerical accuracy. Furthermore, the DD-VMS-ROM improved the G-ROM accuracy at a modest computational overhead. Our numerical investigation showed that ROM had the potential to provide efficient and accurate computational tools to describe vaginal deformations, with the ultimate goal of improving maternal health. Our second study compared two common computational strategies for surrogate modeling, physics-based G-ROM and data-driven machine learning (ML), for decreasing the cost of FE simulations of the ex vivo deformations of rat vaginal tissue subjected to inflation testing to study the effect of a pre-imposed tear. Since there are many methods associated with each modeling approach, to provide a fair and natural comparison, we selected a basic model from each category. From the ROM strategies, we considered a simplified G-ROM that is based on the linearization of the underlying nonlinear FE equations. From the ML strategies, we selected a feed-forward dense neural network (DNN) to create mappings from constitutive model parameters and luminal pressure values to either the FE displacement history (in which case we denote the resulting model ML) or the POD coefficients of the displacement history (in which case we denote the resulting model POD-ML). The numerical comparisons of G-ROM, ML, and POD-ML took place in the reconstructive regime. The numerical results showed that the G-ROM outperformed the ML model in terms of offline central processing unit (CPU) time for model training, online CPU time required to generate approximations, and relative error with respect to the FE models. The POD-ML model improved on the speed performance of the ML, having online CPU times comparable to those of the G-ROM given the same size of POD bases. However, the POD-ML model did not improve on the error performance of the ML. In our last study, we expanded our investigation of ML methods for surrogate modeling by comparing the performance of a DNN similar to what was used previously to that of a convolutional neural network (CNN) using 1-D convolution on the input parameters from FE simulations of active vaginal tearing. The new FE simulations utilized a custom continuum damage model that provided material damage and failure properties to an existing anisotropic hyperelastic constitutive model to replicate experimentally-observed tear propagation behaviors. We employed our DNN and CNN models to create mappings from constitutive model parameters, geometric properties of the propagating tear, and luminal pressure values to either the full FE displacement history or the POD coefficients of the displacement history. The root-mean-square error (RMSE) with respect to the FE displacement history achieved by full order output ML predictions was reproducible with POD-ML using a basis of only dimension l=10. Additionally, an order of magnitude reduction in offline time was observed using POD-ML over full-order ML with minimal difference between DNN and CNN architectures. Differences in online computational costs between ML and POD-ML were found to be negligible, but the DNNs produced predictions slightly faster than the CNNs, though both online times were on the same order of magnitude. While convolution did not significantly aid the regression task at hand, POD-ML was demonstrated to be an efficient and effective approach for surrogate modeling of the FE tear propagation model, approximating the displacement history with RMSE less than 0.1 mm and generating results 7 orders of magnitude faster than the FE model. This set of baseline numerical investigations serves as a starting point for future computer simulations that consider state-of-the-art G-ROM and ML strategies, and the in vivo geometry, boundary conditions, material properties, and tissue damage mechanics of the human vagina, as well as their changes during labor. / Doctor of Philosophy / Computer simulations of childbirth are extremely time-consuming, making them impractical for guiding decision-making by obstetricians when a patient is entering labor. The complex geometry, material microstructure, and large deformations undergone by the vagina during childbirth result in material and geometric properties that are challenging to mathematically model. Consequently, numerical solver methods (e.g., finite elements) require large amounts of time to simulate childbirth. Simplifying assumptions can reduce computational time, but this simplification usually comes at the expense of simulation accuracy. The work of this dissertation proposes the use of several techniques to reduce model complexity and create accurate approximations and predictions of results from full-order models (FOMs) with profound reductions in computational time. Our first study used reduced order models (ROMs) to extract the significant information from a FOM of the rat vagina subjected to inflation. We compared a basic ROM and an advanced, data-driven ROM. Our second study compared the basic ROM to a basic machine learning (ML) technique for approximating a FOM that simulated inflation of the rat vagina with a pre-imposed tear. A hybrid technique incorporating elements of both ROM and ML to approximate FOM results was also considered. Our final study made use of ML and hybrid techniques using a more advanced neural network (a convolutional neural network). These ML models were used to predict the results of a FOM simulation of vaginal tear propagation. These numerical investigations serve as a starting point for future development of computer simulations using state-of-the-art ROM and ML strategies as well as more realistic models for the mechanics of the human vagina during childbirth.