et al. developed Electrocardiography Artificial
Intelligence (ECG-AI) models for predicting fatal
coronary heart disease using 12-lead and single-lead
ECG data, demonstrating the potential of AI for early
detection and intervention (Butler, 2023). Similarly,
Nedadur et al. explored the use of AI in
echocardiography, particularly in valvular heart
disease, demonstrating how AI can improve
diagnostic accuracy through automated analysis
(Nedadur, 2022). Maambo et al. described an AI
medical diagnostic system that utilizes healthcare
data to predict heart disease risk, using data mining
algorithms to improve the diagnostic process
(Maambo, 2022). Janik et al. investigate deep
learning models for cardiac Magnetic Resonance
Imaging (MRI) segmentation, focusing on the
importance of model interpretability to make AI-
assisted diagnosis more transparent and
understandable (Janik, 2021). Finally, Thoenes et al.
discuss the use of AI in the management of aortic
valve disease, emphasizing how AI can support early
diagnosis and efficient treatment planning (Thoenes,
2021). Together, these examples highlight the diverse
and impactful applications of AI in the diagnosis,
treatment, and management of heart disease, marking
an important step in cardiology's use of technology to
improve patient prognosis.
The review is divided into following sections. The
second section firstly provides an overview of current
mainstream methods for cardiac disease diagnosis.
Following this is an analysis of those methods,
including their advantages and disadvantages, the
difficulties they encounter and the main algorithms
they used. The last section is conclusion of the current
AI field of the disease diagnosis, and its future
prospects.
2 METHOD
In this section, the current mainstream AI-based
models for diagnosing heart disease, including
traditional machine learning, as well as deep learning
models, will be demonstrated.
2.1 Framework of AI-Based Heart
Disease Diagnosis
In order to improve the accuracy of diagnosing heart
disease as well as the efficiency of the diagnosis and
to improve the predictive ability, an AI-based
diagnostic framework for heart disease should
contain multiple steps. Typically, a typical
framework is going to contain the following aspects.
The first step is data collection and preprocessing.
The sources of data are generally large datasets
including Electronic Health Records (EHR), imaging
data (e.g., echocardiograms, MRIs, Computed
Tomography (CT) scans), genomics, wearable
devices, and patient-reported data. However,
generally these data are generally not directly
available as inputs to the training model, and before
that, they need to be cleaned, normalized and
anonymized so that the model can recognize them
effectively. This step may include dealing with
missing values, correcting errors and normalizing
formats. Next, the model needs to extract the most
relevant features that will help to accurately diagnose
heart disease (e.g., patient demographics, clinical
parameters, lab results, imaging features) and will
convert the raw data into a format that can be
efficiently processed by AI algorithms, often using
techniques such as Principal Component Analysis
(PCA) for dimensionality reduction or customized
algorithms for extracting meaningful attributes from
complex data such as images. In the third step,
appropriate artificial intelligence models such as
Machine Learning (ML) algorithms (e.g., decision
trees, support vector machines, random forests) and
Deep Learning (DL) models (e.g., convolutional
neural networks for image analysis) are selected
based on the characteristics of the data. Preprocessed
data is fed into selected models to learn patterns
related to heart disease. This involves dividing the
data into training and validation sets to iteratively
improve the accuracy of the model. In order to verify
the validity of the model, it needs to be validated and
tested after the training is completed. Usually, this
step involves tuning the model parameters using a
separate part of the dataset (in the dataset, but not
involved in model training). The performance of the
model on the test set is then evaluated to assess its
generalizability and accuracy in diagnosing heart
disease. Performance metrics may include accuracy,
sensitivity, specificity, and area under the receiver
operating characteristic curve (ROC).
For this type of AI models, there is also a need to
integrate them into the clinical workflow by
integrating the AI models into the clinical
environment, for example by embedding them in
electronic medical record systems or diagnostic tools
to support healthcare professionals. User-friendly
interfaces also need to be developed to enable
clinicians to enter data, receive predictions and
visualize results in an intuitive way.
To ensure that it remains accurate and effective
over time, the developers involved also have to
continuously monitor the performance of the AI