首页|Findings from University of Porto Provides New Data on Machine Learning (A Finit e Element-based Machine Learning Framework To Predict the Mechanical Behavior of the Pelvic Floor Muscles During Childbirth)
Findings from University of Porto Provides New Data on Machine Learning (A Finit e Element-based Machine Learning Framework To Predict the Mechanical Behavior of the Pelvic Floor Muscles During Childbirth)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Porto, Portugal, by Ne wsRx correspondents, research stated, “The medical community has been focusing o n gaining a deeper understanding of birth trauma, which affects millions of wome n worldwide. Maternal lesions can be challenging to diagnose and expensive to ex amine.” Financial support for this research came from Fundacao para a Ciencia e a Tecnol ogia (FCT). Our news editors obtained a quote from the research from the University of Porto , “To better comprehend the mechanism of injuries occurring in the pelvic floor muscles (PFM), biomechanical simulations can be a valuable tool. However, utiliz ing the finite element method (FEM) to conduct simulations can be a time-consumi ng process. To overcome this issue, the present study aims to develop a machine learning (ML) framework to predict stresses on the PFM during childbirth by trai ning ML algorithms on FEM simulation data. To generate the dataset for the ML al gorithm’s training, childbirth simulations were performed using different materi al properties to characterize the PFM. Four ML algorithms were employed, namely Random Forest (RF), Extreme Gradient Boosting (XGBT), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), considering two scenarios: (1) stre ss prediction for the maximum stretch level of the muscle, and (2) for multiple levels of fetal descent. Results showed that the ANN performed best in the forme r, with a mean absolute error (MAE) of 0.191 MPa. In the latter, XGBT provided l ower errors for 20 and 35 mm of fetal descent, with MAE values of 0.002 and 0.02 8 MPa, respectively. Nevertheless, the ANN yielded better predictions for 50 and 65 mm, with MAE values of 0.214 and 0.187 MPa, respectively.”
PortoPortugalEuropeAlgorithmsCyb orgsEmerging TechnologiesMachine LearningUniversity of Porto
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