Modeling and Predicting Blood Flow Characteristics through Double
Stenosed Artery from CFD simulation using Deep Learning Models
- URL: http://arxiv.org/abs/2112.03698v1
- Date: Sat, 4 Dec 2021 11:16:28 GMT
- Title: Modeling and Predicting Blood Flow Characteristics through Double
Stenosed Artery from CFD simulation using Deep Learning Models
- Authors: Ishat Raihan Jamil and Mayeesha Humaira
- Abstract summary: We train deep learning models to learn and predict blood flow characteristics using a dataset generated by CFD simulations of simplified double stenosed artery models.
A novel geometric representation of the constricted neck is proposed which, in terms of a generalized simplified model, outperforms the former assumption.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Establishing patient-specific finite element analysis (FEA) models for
computational fluid dynamics (CFD) of double stenosed artery models involves
time and effort, restricting physicians' ability to respond quickly in
time-critical medical applications. Such issues might be addressed by training
deep learning (DL) models to learn and predict blood flow characteristics using
a dataset generated by CFD simulations of simplified double stenosed artery
models with different configurations. When blood flow patterns are compared
through an actual double stenosed artery model, derived from IVUS imaging, it
is revealed that the sinusoidal approximation of stenosed neck geometry, which
has been widely used in previous research works, fails to effectively represent
the effects of a real constriction. As a result, a novel geometric
representation of the constricted neck is proposed which, in terms of a
generalized simplified model, outperforms the former assumption. The sequential
change in artery lumen diameter and flow parameters along the length of the
vessel presented opportunities for the use of LSTM and GRU DL models. However,
with the small dataset of short lengths of doubly constricted blood arteries,
the basic neural network model outperforms the specialized RNNs for most flow
properties. LSTM, on the other hand, performs better for predicting flow
properties with large fluctuations, such as varying blood pressure over the
length of the vessels. Despite having good overall accuracies in training and
testing across all the properties for the vessels in the dataset, the GRU model
underperforms for an individual vessel flow prediction in all cases. The
results also point to the need of individually optimized hyperparameters for
each property in any model rather than aiming to achieve overall good
performance across all outputs with a single set of hyperparameters.
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