AIMC Topic: Hemodynamics
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Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.
Scientific reports
Jul 2, 2025
To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI) and to demonstrate its pote...
Evaluating artificial intelligence models for rupture risk prediction in unruptured intracranial aneurysms: a focus on vessel geometry and hemodynamic insights.
Neurosurgical review
Jul 2, 2025
The estimation of rupture risk in Unruptured Intracranial Aneurysm (UIA) constitutes a major area of clinical interest due to the significant morbidity and mortality rates associated with aneurysm rupture. Classic clinical models based on factors suc...
Enhancing clinical decision-making in closed pelvic fractures with machine learning models.
Biomolecules & biomedicine
May 8, 2025
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)...
Prevalence of low-flow state by automated machine-learning 3D echocardiography in patients with moderate-to-severe aortic stenosis and normal ejection-fraction.
The international journal of cardiovascular imaging
Apr 10, 2025
BACKGROUND: In aortic valve stenosis (AS), measurement of stroke volume index (SVi) by the 2D Doppler- technique is required to calculate the aortic valve area (AVA) and determine flow status but is prone to systematic errors.
Automation
Echocardiography, Doppler
Blood Flow Velocity
Echocardiography, Three-Dimensional
Aortic Valve
Predictive Value of Tests
Image Interpretation, Computer-Assisted
Male
Ventricular Function, Left
Stroke Volume
Prevalence
Severity of Illness Index
Hemodynamics
Aortic Valve Stenosis
Female
Reproducibility of Results
Aged
Aged, 80 and over
Humans
Machine Learning
InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Apr 2, 2025
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
Mar 31, 2025
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
Mar 26, 2025
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
Mar 24, 2025
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
Feb 20, 2025
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 ...
Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD.
Medical & biological engineering & computing
Feb 17, 2025
Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to rupture. Therefore, precise WSS determination on the aneurysm is crucial for rupture risk assessment....
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