The world of data-driven technologies has also entered the field of biomechanics. The flexibility of these techniques is remarkable as is their requirement for large, well-annotated data sets. The first applications in Cardiology were in the field of imaging, borrowing techniques developed from computer vision. Recently, more sophisticated technologies such as Natural Language Processing (NLP), Recurrent Neural Networks (RNN), and Transformers have been applied, as well as Deep Neural Networks with remarkable results.
In this Research Topic, we will describe the most sophisticated application of Machine Learning techniques, and their capability to learn to reconstruct important dynamics of the process from the biomechanical data without having to solve complex non-linear fluid-structure-interaction problems. This promises to offer almost instantaneous applications in the diagnosis of plaque rupture, pressure drops over stenosis, and shear stress distributions.
Sub-themes for papers include, but are not limited to:
1) Articles presenting ROM, DOM, and other methods developed for solving complex computational fluid dynamic and solid mechanic equations.
2) In vivo studies examining the efficacy of these methods in assessing the distribution of the local hemodynamic forces in different vascular beds.
3) Clinical research articles evaluating the role of data-driven methods in evaluating the structural stress distribution in coronary the aorta and peripheral arteries.
4) The first applications of ML techniques in predicting vessel wall response to treatment.
The world of data-driven technologies has also entered the field of biomechanics. The flexibility of these techniques is remarkable as is their requirement for large, well-annotated data sets. The first applications in Cardiology were in the field of imaging, borrowing techniques developed from computer vision. Recently, more sophisticated technologies such as Natural Language Processing (NLP), Recurrent Neural Networks (RNN), and Transformers have been applied, as well as Deep Neural Networks with remarkable results.
In this Research Topic, we will describe the most sophisticated application of Machine Learning techniques, and their capability to learn to reconstruct important dynamics of the process from the biomechanical data without having to solve complex non-linear fluid-structure-interaction problems. This promises to offer almost instantaneous applications in the diagnosis of plaque rupture, pressure drops over stenosis, and shear stress distributions.
Sub-themes for papers include, but are not limited to:
1) Articles presenting ROM, DOM, and other methods developed for solving complex computational fluid dynamic and solid mechanic equations.
2) In vivo studies examining the efficacy of these methods in assessing the distribution of the local hemodynamic forces in different vascular beds.
3) Clinical research articles evaluating the role of data-driven methods in evaluating the structural stress distribution in coronary the aorta and peripheral arteries.
4) The first applications of ML techniques in predicting vessel wall response to treatment.