INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 129
Artificial Intelligence (AI) in Cardiovascular Diseases Detection
Shital A. Ladkat
Department of Electronics, Dr. D. Y. Patil Arts, Commerce and Science, Pimpri, Pune, Maharashtra, India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1413SP028
Received: 26 June 2025; Accepted: 30 June 2025; Published: 24 October 2025
Abstract—AI is significantly impacting cardiovascular disease diagnosis and management, enhancing accuracy, speed, and early
detection. AI algorithms can analyze ECGs, imaging data, and other clinical information to identify heart conditions, predict
risks, and personalize treatment strategies. This includes detecting structural heart diseases like hypertrophic cardiomyopathy and
aortic stenosis, as well as predicting long-term outcomes for heart failure patients.
Keywords—ECG Sample, AI.
I. Introduction
An intelligence exhibited by machines, idea of intelligence being incorporated into inanimate objects has been floated since
antiquity. AI programs were developed in 1951 to play checkers and chess. AI has expanded to almost every facet of modern
life, including the medical field. Mayo Clinic is a leader in the movement to bring artificial intelligence (AI) tools and technology
into clinical practice to benefit people who have or are at risk of heart disease. The clinic's AI cardiology team is applying these
new approaches to early risk prediction and diagnosis of serious or complex heart problems. People who receive heart care from
Mayo Clinic's Department of Cardiovascular Medicine may benefit from access to the clinic's leading-edge research and expertise
in AI cardiology to improve patient care. Detecting heart disease, treating strokes faster and enhancing diagnostic radiology
capabilities by AI.
This Basic of Artificial Intelligence
The ability to make computers or machines learn to solve problems that would otherwise require human effort called as Artificial
intelligence. Advances in computing power have made it possible to analyze large amounts of data quickly with consistency and
accuracy. AI has enabled health care scientists to apply AI to huge, complex data sets in a way that improves decision-making,
diagnosis and treatment by detecting patterns in patient data. The basic building block of an AI system is a "neural network." For
example, a computer system is trained by ingesting and analyzing hundreds of thousands of sets of similar readings. It becomes
experienced in looking at a focused problem, such as ECGs. The result is that an AI system can read a simple test, detect a heart
condition and predict possible future problems.
ECG Signal: -
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 130
Cathlab Working
Artificial intelligence-enabled ECG screening for asymptomatic left ventricular dysfunction
The P wave indicates atrial depolarization (contraction), the QRS complex represents ventricular depolarization (contraction), and
the T wave signifies ventricular repolarization (relaxation).
P Wave:
This small, upward deflection represents the electrical impulse traveling through the atria, causing them to contract and pump
blood into the ventricles.
QRS Complex:
This is a larger, sharper wave, indicating ventricular depolarization. The ventricles are the heart's main pumping chambers, so
this represents the force with which they contract and push blood out to the body.
T Wave:
This wave represents ventricular repolarization, meaning the ventricles are returning to their resting state after contraction. It's
a recovery phase for the ventricles
AI is significantly impacting cardiovascular disease diagnosis and management, enhancing accuracy, speed, and early
detection. AI algorithms can analyze ECGs, imaging data, and other clinical information to identify heart conditions, predict
risks, and personalize treatment strategies. This includes detecting structural heart diseases like hypertrophic cardiomyopathy and
aortic stenosis, as well as predicting long-term outcomes for heart failure patients.
The applications of AI in heart disease:
1. Diagnosis and Early Detection:
ECG Analysis:
AI algorithms can analyze electrocardiograms (ECGs) to detect abnormalities that may indicate heart disease, even in
individuals with normal ECGs.
Cardiac Imaging:
AI can assist in the analysis of echo cardiograms, cardiac MRI, and CT scans to evaluate heart function and identify structural
abnormalities.
Wearable Technology:
AI-powered wearable devices like smart watches can monitor heart rate, rhythm, and other vital signs, enabling early detection
of heart conditions and improving patient care.
Risk Stratification:
AI models can assess an individual's risk of developing heart disease based on a combination of factors, including genetics,
lifestyle, and medical history.
2. Treatment and Management:
Personalized Treatment:
AI can help optimize treatment strategies by identifying the most effective medications, procedures, or devices for individual
patients.
Predictive Modeling:
AI models can predict the likelihood of adverse outcomes, such as heart failure readmission or mortality, allowing for more
proactive management of patients.
Surgical Planning:
AI can assist in planning and performing cardiac surgery by visualizing the heart's anatomy and predicting surgical outcomes.
3. Specific Examples:
Left Ventricular Dysfunction:
AI-powered screening tools have demonstrated high accuracy in detecting left ventricular dysfunction, a condition where the
heart's pumping ability is weakened.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 131
Circadia V:
A 14-year-old student developed an AI app called CircadiaV that can detect heart diseases with high accuracy.
Peripartum Cardiomyopathy:
AI can be used to predict heart muscle weakness in pregnant women, enabling early detection and intervention.
4. Future Directions:
Audio Analysis:
Researchers are exploring the use of AI to analyze heart sounds and other auditory signals for diagnostic purposes.
Cloud Computing:
AI algorithms are being trained on large datasets of medical images and records, enabling more accurate and efficient
analysis.
AI-Driven Devices:
AI-powered devices are being developed to monitor heart health continuously and provide real-time feedback to patients and
clinicians.
5. Limitations and Challenges:
Data Bias:
AI models can be biased if trained on datasets that don't accurately reflect the diversity of the population.
Interpretability:
Some AI models are difficult to interpret, making it challenging for clinicians to understand why they make certain
predictions.
Ethical Considerations:
Ethical issues related to AI in healthcare, such as data privacy and algorithmic bias, need to be addressed.
Bringing artificial intelligence (AI) into clinical practice to bring the benefits AI to people with diseases of the heart and blood
vessels done by Heart doctors and scientists work together. Mayo Clinic is a leader in the movement to bring artificial intelligence
(AI) tools and technology into clinical practice to benefit people who have or are at risk of heart disease. The
clinic's AI cardiology team is applying these new approaches to early risk prediction and diagnosis of serious or complex heart
problems. People who receive heart care from Mayo Clinic's Department of Cardiovascular Medicine may benefit from access to
the clinic's leading-edge research and expertise in AI cardiology to improve patient care.AI is intelligence exhibited by machines
who touches almost every facet of modern life, including medicine. AI is being used at Mayo Clinic to program computers and
goal is to process and respond to data quickly and consistently for better treatment outcomes. Uses for AI include detecting heart
disease, treating strokes faster and enhancing diagnostic radiology capabilities. These technologies complement the knowledge of
doctors. Ideally, by bringing together direct care and data analysis, AI cardiology allows doctors to spend more time with their
patients and improves the shared decision-making process
Artificial intelligence-enabled ECG screening for asymptomatic left ventricular dysfunction
Some common examples of machines that utilize versions of AI include:
iRobot Roomba, which vacuums the floor and can navigate around obstacles
Mars rovers Spirit and Opportunity
Siri, Apple's virtual assistant
AI operate within a restricted range of functions to accomplish narrowly demarcated tasks. Deep learning ― strong AI ― is one
of a family of machine learning methods based on learning data set representations. Advances in computing power so that large
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 132
amounts of data can be quickly analyzed have made the application of AI to huge, complex data sets feasible. Deep learning
architectures have been applied to diverse fields such as speech recognition, social network filtering, bioinformatics, drug design
and medical image interpretation.
Deep neural systems comprise a series of layers:
An input layer
A cascade of processing units or hidden layers
An output layer
Recurrent neural network Enlarge image
Each of the layers comprises individual neurons that extract and transfer data in a hierarchical fashion into more composite
representations. Data from one layer is processed and fed into the next layer in a recurrent neural network. Different types of
neural networks have been developed; the type of neural network employed depends on the type and complexity of analysis being
performed.
Convoluted neural network Enlarge image
Pooling examples Enlarge image
Neural networks are not particularly good at image analysis, convolutional neural networks (CNNs) are commonly used for this
function and are especially helpful in the evaluation of high-resolution medical imaging.
The hidden layer fed data from the input layer which is in feature extraction (convolutional layers), which perform a series of
convolutions, which are mathematical functions, and pooling operations which make assumptions about the data to downsize the
number of parameters to analyze and neutralize the effect of changes in scale or orientation; computational cost is also reduced.
Characteristic features in the image are detected during this process. For example, in a picture of a pig, this extraction process
identifies four short legs with even-toed ungulate hooves, a curly tail, fat body, two pointy ears and a snout. In image
classification (fully connected layers), the fully connected layers, in which each neuron in one layer is connected to every neuron
in the adjacent layers, will classify the image based upon these extracted features. Electro physiologist and chair of
Cardiovascular Medicine at Mayo Clinic in Rochester, Minnesota, spearheaded the study utilizing CNNs to analyze ECG to
predict the presence of asymptomatic left ventricular dysfunction (ALVD).
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 133
Risk increases with age; in the elderly, ALVD is present in 9% of individuals. Applying artificial intelligence to some of the most
challenging clinical problems. Exciting examples include:
Early risk prediction of conditions such as embolic stroke
Heart monitoring and arrhythmia detection in smart clothing projects based on a textile computing platform
Occult disease detection, such as identifying atrial fibrillation's earliest, sub clinical stages, through heart physiology
signals transmitted by mobile ECG.
References
1. Shital L. Pingale, Nivedita Daimiwal,” Detection of Various Diseases Using ECG Signal in MALAB “International Journal of
Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-3, Issue-1, March 2014
2. Bringing AI-Assisted Cardiology into the Future, August 3, 2023
3. Xiaoyu Sun, Yuzhe Yin, Qiwei Yang ,Tianqi Huo,” Artificial intelligence in cardiovascular diseases: diagnostic and
therapeutic perspectives” European Journal of Medical Research volume 28, Article number: 242 (2023).