AIMC Topic: Hemodynamics

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Expanding point cloud statistical shape model applications: Generalized vascular modeling for population-level hemodynamic simulations.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Population-scale hemodynamic research faces limitations due to the trade-off between computationally expensive patient-specific Computational Fluid Dynamics (CFD) and overly idealized cylindrical models. To overcome this, we...

Reconstructing cerebral hemodynamics from sparse data using Neural Operator Transformers.

Computers in biology and medicine
Cardiovascular diseases remain a major cause of mortality and disability, underscoring the need for improved analysis of brain hemodynamics. The Circle of Willis plays a crucial role in maintaining cerebral blood flow; however, conventional measureme...

Learning hemodynamic scalar fields on coronary artery meshes: A benchmark of geometric deep learning models.

Computers in biology and medicine
Coronary artery disease involves the narrowing of coronary vessels due to atherosclerosis and is currently the leading cause of death worldwide. The gold standard for its diagnosis is the fractional flow reserve (FFR) examination, which measures the ...

Enhancing clinical decision-making in closed pelvic fractures with machine learning models.

Biomolecules & biomedicine
Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML)...

InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Estimation of cardiovascular model parameters from electronic health records (EHRs) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to ...

Parallel convolutional neural networks for non-invasive cardiac hemodynamic estimation: integrating uncalibrated PPG signals with nonlinear feature analysis.

Physiological measurement
Understanding cardiac hemodynamic status (CHS) is essential for accurate cardiovascular health assessment, as it is governed by key parameters such as cardiac output (CO), systemic vascular resistance (SVR), and arterial compliance (AC). This study a...

Role of physics-informed constraints in real-time estimation of 3D vascular fluid dynamics using multi-case neural network.

Computers in biology and medicine
Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research to model flow in blood vessels and airways. It is further useful to some clinical applications, such as predicting arterial fractional flow reserves,...

The future of artificial intelligence in cardiovascular monitoring.

Current opinion in critical care
PURPOSE OF REVIEW: Cardiovascular monitoring is essential for managing hemodynamic instability and preventing complications in critically ill patients. Conventional monitoring approaches are limited by predefined thresholds, dependence on clinician e...

Effect of Parallel Cognitive-Motor Training Tasks on Hemodynamic Responses in Robot-Assisted Rehabilitation.

Brain connectivity
Previous studies suggest that the combination of robot-assisted training with other concurrent tasks may promote the functional recovery and improvement better than the single task. It is well-established that robot-assisted rehabilitation training ...