Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simulation algorithms. Here, we integrate computational fluid dynamics with our customized analysis framework based on a multi-attribute point cloud dataset and physics-informed neural networks (PINNs)-aided deep learning modules. This combination is implemented by our workflow that generates flow field datasets within two types of patient personalized models - aorta with fine coronary branches and abdominal aorta. Deep learning modules with or without an antecedent hierarchical structure model the flow field development and complete the mapping from spatial and temporal dimensions to 4D hemodynamics. 88,000 cases on 4 randomized partitions in 16 controlled trials reveal the hemodynamic landscape of spatio-temporal anisotropy within two types of personalized models, which demonstrates the effectiveness of PINN in predicting the space-time behavior of flow fields and gives the optimal deep learning framework for different blood vessels in terms of balancing the training cost and accuracy dimensions. The proposed framework shows intentional performance in computational cost, accuracy and visualization compared to currently prevalent methods, and has the potential for generalization to model flow fields and corresponding clinical metrics within vessels at different locations. We expect our framework to push the 4D hemodynamic predictions to the real-time level, and in statistically significant fashion, applicable to morphologically variable vessels. 

Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology

Reliability-based optimization of multiple Folded Pendulum TMDs through Efficient Global Optimization

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers.

A new active-learning estimation method for the failure probability of structural reliability based on Kriging model and simple penalty function

The hydrodynamic performance and cavitation development in centrifugal pump have a decisive impact on its energy conversion and performance. However, there are still bottlenecks when using current experimental methods and simulation algorithms in the real-time measurement and visual display of flow fields, and the high experimental and computational cost cannot be ignored. Here, we integrated computational fluid dynamics (CFD) and experimental platform with our customized framework based on a multi-attribute point cloud dataset and advanced deep learning network. This combination is made possible by our workflow to generate simulated data of flow characteristics of cavitation in the pump as the training/ test dataset, complete the deep learning algorithm process and check the consistency with the experimental results. Deep learning models the multiphase flow system of centrifugal pump and completes the mapping from the structure of pump and working conditions to the cavitation, pressure, and velocity field. The statistical analysis shows that predictions results are in agreement with the CFD method, but the calculation time is greatly reduced. Compared to the prevalent methods, the proposed deep learning framework shows superior performance in accuracy, computational cost, visual display and has the potential of generality to model the interaction between different fluids and impellers. 

Deep learning, numerical, and experimental methods to reveal hydrodynamics performance and cavitation development in centrifugal pump

A machine learning strategy for fast prediction of cardiac function based on peripheral pulse wave

A data-driven Kriging model based on adversarial learning for reliability assessment

Abstract Background: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. Methods: Forty-eight post-operative children, median age 4 months (2.9–11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33–197), mean lactate level at admission of 22.3 mg/dL (range 6.3–71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance. Results: The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw. Conclusions: The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/84C7FF0546A96688A071B6382979146A/S1047951122000932a.pdf/div-class-title-machine-learning-predicts-blood-lactate-levels-in-children-after-cardiac-surgery-in-paediatric-icu-div.pdf

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

Objective. This study aims to accurately identify the effects of respiration on the hemodynamics of the human cardiovascular system, especially the cerebral circulation. Approach: we have developed a machine learning (ML)-integrated zero–one-dimensional (0–1D) multiscale hemodynamic model combining a lumped-parameter 0D model for the peripheral vascular bed and a one-dimensional (1D) hemodynamic model for the vascular network. In vivo measurement data of 21 patients were retrieved and partitioned into 8000 data samples in which respiratory fluctuation (RF) of intrathoracic pressure (ITP) was fitted by the Fourier series. ML-based classification and regression algorithms were used to examine the influencing factors and variation trends of the key parameters in the ITP equations and the mean arterial pressure. These parameters were employed as the initial conditions of the 0–1D model to calculate the radial artery blood pressure and the vertebral artery blood flow volume (VAFV). Main results: during stable spontaneous respiration, the VAFV can be augmented at the inhalation endpoints by approximately 0.1 ml s−1 for infants and 0.5 ml s−1 for adolescents or adults, compared to those without RF effects. It is verified that deep respiration can further increase the ranges up to 0.25 ml s−1 and 1 ml s−1, respectively. Significance. This study reveals that reasonable adjustment of respiratory patterns, i.e. in deep breathing, enhances the VAFV and promotes cerebral circulation.

Impacts of respiratory fluctuations on cerebral circulation: a machine-learning-integrated 0–1D multiscale hemodynamic model

Athletes often suffer from knee joint injuries because they often use the knee joint to exert force during training. This paper aims to analyze and discuss the auxiliary diagnosis of the knee joint sports injury detection model based on machine learning. This paper expounds the treatment method of knee joint injury, and proposes a machine learning algorithm. On the basis of this research, the auxiliary diagnosis experiment of the knee joint sports injury detection model is analyzed. The experimental results show that after 3 months of machine learning-based rehabilitation training, there is a significant difference in the duration of the balance pad before and after the table tennis players practice. The duration of the athletes on the balance mat has increased, and the increase is relatively large. Among them, the average duration of female athletes on the balance mat increased from 75.5 seconds before training to 141.9 seconds after training, while the average duration of male athletes on the balance mat increased from 66.7 seconds before training to 136.8 seconds after training. Studies have shown that machine learning-based rehabilitation physical training can significantly improve athletes' endurance on balance mats and can improve knee function scores. In summary, machine learning-based rehabilitation physical training can effectively improve knee joint injuries.

/pdf/value-evaluation-of-knee-joint-sports-injury-detection-model-4x1e8rll.pdf

Value evaluation of knee joint sports injury detection model-aided diagnosis based on machine learning

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