Application of Artificial Intelligence in Stroke Prediction: Latest
Advancements and Future Prospects
Boxiang He
a
Computer Science, Wenzhou Kean University, Wenzhou, China
Keywords: Artificial Intelligence, Brain Stroke, Machine Learning, Deep Learning.
Abstract: Some scientific and technological methods are more and more applied in medical treatment, and great
achievements have been made in the prediction of stroke, but there are still great challenges. This paper
explores the intersection of stroke management and Artificial Intelligence (AI), focusing on recent
advancements, methodologies, limitations, and future prospects. Stroke, characterized by disrupted blood
flow to the brain, necessitates swift diagnosis and intervention to mitigate potential cell damage or death.
Traditional machine learning algorithms such as Support Vector Machine (SVM) and Random Forest, along
with deep learning algorithms like Artificial Neural Networks (ANN) and Convolutional Neural Networks
(CNN), have been employed to construct predictive models for stroke diagnosis and prognosis. However,
challenges including interpretability, privacy concerns, and applicability across diverse healthcare settings
persist. Solutions such as Shapley Additive Explanations (SHAP), federated learning, and transfer learning
have been proposed to address these challenges and enhance the trustworthiness and generalizability of AI-
driven approaches in stroke management. Continued research efforts are necessary to overcome limitations,
expand sample sizes, and enhance the accuracy and efficiency of AI models in predicting and analyzing
strokes, ultimately improving patient outcomes in stroke management.
1 INTRODUCTION
A stroke, also known as a brain stroke, happens when
blood flow to part of the brain is disrupted, causing
deprivation of oxygen and nutrients, leading to
potential cell damage or death within minutes.
Strokes manifest in various symptoms, such as
weakness, numbness, difficulty speaking, vision
problems, severe headache, and loss of balance.
Recent advancements in deep learning technology
have significantly influenced stroke diagnosis and
treatment in the medical field. Deep learning
algorithms excel in accurately analyzing medical
images to identify stroke lesions, aiding in faster
diagnosis and treatment planning. Additionally, deep
learning can utilize large-scale clinical data to predict
stroke occurrence, enabling early intervention and
prevention (Lee, 2017). Pre-trained models further
enhance medical image analysis, improving
diagnostic accuracy and efficiency. In summary, the
integration of deep learning technology offers
promising prospects for precise and effective stroke
a
https://orcid.org/0009-0001-9327-5761
diagnosis, treatment, and prevention, potentially
improving patient outcomes.
Recent advancements in the intersection of brain
strokes and Artificial Intelligence (AI) showcase a
promising frontier in healthcare (Qiu, 2022). AI
technologies are being increasingly employed across
various facets of stroke management, catalyzing
significant improvements in patient care. Initially, AI
algorithms are revolutionizing stroke diagnosis by
swiftly and accurately analyzing medical imaging
scans, including Magnetic Resonance Imaging
(MRIs) and Computed Tomography (CT) scans. This
capability enables clinicians to promptly identify
stroke symptoms and initiate timely interventions,
thereby potentially minimizing long-term damage.
Moreover, machine learning models harness vast
datasets of patient records to predict individual stroke
risks, facilitating proactive measures for prevention
and intervention (Soun, 2021). Additionally, AI
systems leverage real-time patient data and medical
literature to generate personalized treatment plans
tailored to each patient's unique needs. By optimizing
He, B.
Application of Artificial Intelligence in Stroke Prediction: Latest Advancements and Future Prospects.
DOI: 10.5220/0012960700004508
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 617-621
ISBN: 978-989-758-713-9
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
617
treatment strategies, AI contributes to enhancing
patient outcomes and reducing healthcare costs (Haris,
2018). According to a 2017 study, machine learning
has been widely used in stroke imaging, including the
two main methods of Artificial Neural Network
(ANN) and Convolutional Neural Network (CNN). In
addition, according to a 2018 study, artificial
intelligence is also used to analyze data on various
brain diseases. great effect, but still has limitations
Furthermore, AI-driven rehabilitation tools are aiding
stroke survivors in regaining motor function and
improving overall recovery trajectories. These tools,
which incorporate robotics, virtual reality, and
personalized exercise regimens, offer tailored support
to each patient's rehabilitation journey. Lastly, AI-
enabled telemedicine platforms are facilitating
remote monitoring of stroke patients, allowing
healthcare providers to deliver timely interventions
and support, even in underserved areas. This remote
monitoring capability minimizes the need for in-
person visits, thereby increasing accessibility to
quality stroke care. In summary, the integration of AI
into stroke management holds immense promise for
revolutionizing diagnosis, treatment, rehabilitation,
and remote monitoring, ultimately leading to better
patient outcomes and healthcare delivery (Dritsas,
2022).
The remainder of this article will delve into the
method, discussion, results, and conclusion. Firstly,
the method section will meticulously review and
encapsulate the research methodologies employed in
studying the intersection of stroke and AI in recent
years. The forthcoming investigation will undertake a
meticulous examination of diverse methodologies
employed within the realms of stroke detection,
prediction, treatment optimization, rehabilitation, and
telemedicine. This scrutiny will encompass an
analysis of the datasets utilized, the algorithms
deployed, and the evaluation criteria employed across
these studies. Subsequently, the ensuing discussion
segment will undertake a critical evaluation of these
methodologies, delineating their respective strengths
and weaknesses. Particular emphasis will be placed
on elucidating the encountered limitations,
identifying potential areas for enhancement, and
envisioning future prospects for furthering
technological advancements. This discourse will
encompass a comprehensive exploration of the
challenges inherent in the integration of artificial
intelligence into stroke management practices, whilst
offering insights into strategies to mitigate these
challenges and augment the efficacy of AI-driven
approaches. Lastly, the concluding remarks will
consolidate and synthesize the key findings and
insights derived from the preceding sections. This
will involve furnishing a comprehensive overview of
the paper's contributions to the academic domain,
delineating avenues for prospective research
endeavors, and underscoring the imperative nature of
ongoing innovation in harnessing AI to ameliorate
stroke care practices.
2 METHOD
2.1 Traditional Machine Learning
Algorithms
In recent years, machine learning has rapidly
developed and developed in a variety of applications
in various healthcare systems (Bi, 2019). Machine
learning is a branch of artificial intelligence that
focuses on giving computer systems the ability to
learn and improve automatically without explicit
programming instructions. It can extract patterns and
regularities from large amounts of data to
autonomously infer, generalize, and predict future
behavior. Its goal is to improve task performance by
letting machines learn from experience. Machine
learning is of great help in the treatment and research
of stroke. It helps analyze data and build models to
facilitate doctors to treat faster and more accurately.
Early detection of stroke is a critical step in effective
treatment, and machine learning can be of huge value
in this process, which is the ultimate technology that
can help medical professionals make clinical
decisions and predictions (Sirsat, 2020).
2.1.1 Random Forest
Random forest is an integrated learning method that
integrates the prediction results of each tree to
improve the accuracy and robustness of the model by
constructing multiple decision trees and using
random sampling and random feature selection. It is
suitable for classification and regression tasks. And it
has the characteristics of high parallelism and
resistance to overfitting. The application of random
forest in stroke research mainly includes two aspects:
prediction and diagnosis of stroke, and rehabilitation
and prognosis assessment after stroke. In terms of
prediction and diagnosis, random forests can use
patients' clinical data, imaging characteristics, etc. to
predict stroke risk and diagnose conditions,
improving the accuracy and timeliness of diagnosis
(Steven, 2017). In addition, it can also be used to
guide the formulation and evaluation of rehabilitation
treatment plans, thereby improving the rehabilitation
effect and prognosis quality. These applications
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provide important support and guidance for early
intervention, personalized treatment and
rehabilitation of stroke (Carlos, 2021).
2.1.2 Support Vector Machine (SVM)
Support Vector Machine (SVM) is a machine learning
method commonly used for classification and
regression analysis. The main idea is to find an
optimal hyperplane that separates sample points of
different categories.
In classification problems, the goal of SVM is to
find a decision boundary that can move samples of
different categories as far away from the hyperplane
as possible, which can increase the robustness of
classification. In order to achieve this goal, SVM
defines the decision boundary through support
vectors (support samples), which are the sample
points closest to the hyperplane. SVM determines the
optimal decision boundary by maximizing the
distance (i.e., margin) between the support vector and
the hyperplane. This distance is also called margin
maximization. In a 2017 study, there were examples
of SVM applied to brain imaging, which is also a
possibility that SVM can be applied to stroke
(Cuingnet, 2010).
2.2 Deep Learning Algorithms
Moreover, in recent years, deep learning algorithms
have also been of great help in the study of stroke.
Deep learning algorithm is an artificial intelligence
technology that learns the characteristics and patterns
of data through a multi-level neural network structure.
These neural networks are composed of a large
number of neurons, each of which is connected to the
neurons of the previous layer. Through an iterative
process, the connection weights between neurons are
constantly adjusted to maximize the accuracy of
predicting or classifying the input data. Through deep
learning algorithms, the system can automatically
discover complex patterns in data and perform
efficient feature extraction and abstraction to achieve
various intelligent tasks such as image recognition,
speech recognition, and natural language processing.
Wang et al. have applied deep learning algorithms in
pathological image analysis in 2019 (Wang, 2019).
These algorithms not only have high accuracy, but
also have high computational efficiency. These
methods can also be applied to analyze images of
strokes.
2.2.1 ANN
In the treatment and research of stroke, ANN has also
played a certain role. It represents a computing model
that imitates the structure and function of biological
neural networks. Artificial neural networks are
composed of a large number of artificial neurons (or
nodes) that are connected to each other through
connections (or edges) to form a network. Each
neuron receives input signals from other neurons,
performs a weighted summation of these signals by
weights, and then passes the result to an activation
function for processing, ultimately producing an
output. ANN is usually trained through optimization
methods such as backpropagation algorithm and
gradient descent to adjust the connection weights
between neurons so that the network can learn and
adapt to the patterns and characteristics of the input
data to achieve various tasks such as classification,
regression, Clustering etc. In 2019, Chen et al.
analyzed the feasibility of ANN for stroke risk
stratification and concluded that ANN is very
effective in predicting stroke (Chan, 2019).
The structure of an Artificial Neural Network
consists of the input layer, hidden layers, and output
layer. The input layer receives external input data,
with each node representing a feature of the input
data. The hidden layers, composed of multiple layers
of neurons, learn the complex relationships between
input data through adjustment of connection weights
and bias parameters. Eventually, the output layer
generates the final output of the model, with the
number of nodes determined by the type of task the
model performs. During training, ANN utilizes the
backpropagation algorithm to continuously adjust
connection weights and biases to minimize the loss
function and enhance the model's performance. This
network structure and learning algorithm enable
ANN to effectively handle various types of data,
performing prediction and classification tasks, and
find wide applications across multiple domains,
including stroke analysis.
2.2.2 CNN
Convolutional Neural Network is an artificial neural
network model that specializes in processing grid-
structured data (such as images, audio). Its core
components include convolutional layers, pooling
layers and fully connected layers. The convolutional
layer extracts the features of the input data by sliding
the convolution kernel, the pooling layer reduces the
size of the feature map and enhances the translation
invariance of the model, and finally performs
classification or other tasks through the fully
connected layer. CNN has achieved great success in
the field of computer vision and is widely used in
tasks such as image recognition and target detection.
Application of Artificial Intelligence in Stroke Prediction: Latest Advancements and Future Prospects
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It has also demonstrated promise in areas like natural
language processing, particularly in the analysis of
images related to strokes. Identifying the location of
an ischemic stroke in CT images can be challenging,
as it's not always clearly visible. Consequently,
diagnosis often depends on a physician's assessment
of the images. CNN can be highly effective in aiding
with this task. In 2017, Lin et al. studied the accuracy
of CNN in identifying ischemic stroke. The accuracy
rate was as high as 90%, which shows that CNN has
great potential in predicting cerebral stroke (Chin,
2017).
The structure of a Convolutional Neural Network
comprises several key components. Firstly,
convolutional layers extract features from input data
using filters, generating feature maps. Pooling layers
reduce spatial dimensions while preserving essential
information. Fully connected layers connect extracted
features to the output layer for classification or
regression tasks. Activation functions introduce non-
linearity to the network. Batch normalization layers
enhance training stability by normalizing feature
maps. Through the combination and stacking of these
components, CNNs effectively extract features from
input data, enabling efficient processing and learning
of complex data such as images and audio.
3 DISCUSSIONS
Although significant progresses have been made, the
application of AI in the context of brain stroke
diagnosis and treatment presents several limitations
and challenges. Firstly, interpretability of AI models
remains a significant concern, particularly in complex
medical scenarios such as stroke diagnosis, where
clinicians require clear explanations for model
predictions to make informed decisions. Secondly,
privacy concerns arise due to the sensitive nature of
medical data, including patient imaging scans and
health records, necessitating robust data protection
measures. Thirdly, the applicability of AI algorithms
across diverse healthcare settings and patient
populations poses challenges due to variations in data
quality, accessibility, and clinical practices. Fourth,
there are limitations in the research on predicting risk
factors for various types of strokes (Bandi, 2020).
To address these challenges, several solutions
have been proposed. For interpretability, techniques
such as Shapley Additive Explanations (SHAP)
provide insights into model predictions, enabling
clinicians to understand the rationale behind AI
recommendations. Privacy concerns can be mitigated
through the implementation of privacy-preserving AI
approaches, including techniques such as federated
learning, which allows model training on
decentralized data without exchanging raw data
between institutions. Additionally, ensuring the
applicability of AI algorithms involves developing
adaptable models that can accommodate variations in
data sources and clinical contexts, as well as fostering
collaborations between AI researchers and healthcare
professionals to tailor solutions to specific clinical
needs. In addition, some models such as random
forests can also help to accurately predict risk factors,
and it is possible to use image datasets to derive
different types of strokes and risk levels in the future
(Bandi, 2020).
Looking ahead, the prospects of AI in brain stroke
management are promising. SHAP and similar
interpretability techniques will continue to evolve,
providing deeper insights into AI decision-making
processes and enhancing trust in AI-driven clinical
decision support systems. Expert systems that
combine domain expertise with AI algorithms hold
potential for personalized stroke management by
integrating clinical guidelines and patient-specific
data. Federated learning offers a pathway for
collaborative model development across institutions
while preserving data privacy, facilitating the creation
of robust and generalizable stroke prediction models.
Furthermore, transfer learning techniques enable the
transfer of knowledge from related tasks or domains
to improve the performance of AI models in stroke
diagnosis and prognosis. Overall, the continued
advancement and integration of these AI approaches
hold great promise for enhancing stroke care
outcomes and reducing the burden of this devastating
neurological condition.
4 CONCLUSIONS
In this work, this paper summarized research on AI's
prediction of stroke and how it can help in this regard.
In the previous prediction and analysis of stroke,
various traditional machine learning (SVM and
random forest) and deep learning algorithms (ANN
and CNN) were used to construct a model that
improved the accuracy and efficiency of prediction.
In the process, it can be found that there are still some
limitations and deficiencies, and the sample size is not
comprehensive, which means that in the future, the AI
model for predicting and analyzing stroke can be
improved, and more samples can be obtained to
improve the accuracy of prediction.
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