Artificial Intelligence in Cardiology
Artificial Intelligence in Cardiology


Artificial intelligence and machine learning methods have played a vital role in every field to create ease for humans. And Now Artificial Intelligence is playing role in Cardiology as well. In this paper, relevant aspect of artificial intelligence is discussed to provide a guide for clinicians. Also, the artificial intelligence-based studies for clinical cardiology are reviewed in this paper. In modern medicine, decision-making is difficult and should be based on structured knowledge and available data. Automated artificial intelligence-based algorithms build decisions by extracting meaningful information from the data that help in the clinical decision-making process.


In modern medicine, a very complex decision-making process is based on the proper interpretation of available facts, reliable evidence, and immediate access to knowledge. However, practicing medicine in the real world has taught us that particular patients’ decisions may not always be objective, and evidence is not always available. The incorporation of artificial intelligence tools such as machine learning and deep learning improves the process of decision-making. Artificial Intelligence techniques also provide a set of tools in cardiology to increase the effectiveness of cardiologists.

There are many medical imaging modalities available, namely magnetic resonance imaging(MRI), electrocardiogram (ECG), Computed tomography (CT), that facilitate the diagnosis of different cardiovascular diseases by applying different AI-based techniques. [1]The invention of medical imaging modalities has played a great role in cardiovascular medicine. The diagnosis and treatment of several cardiovascular diseases rely on the available data. The data may include physical examination, non-invasive imaging diagnostics, laboratory data, invasive angiography, and patient history.

Cardiologists have to perform increasingly sophisticated analyses with the invention of data-rich technologies, including implantable and wearable recording devices, mobile telemetry devices, biometrics, and other patient-related data. [2].Cardiovascular treatment has faced the pressure to achieve tripe aim: improve diagnostics, reduced costs and optimize patient care. So without the help of any clinical decision support tool, the volume of data required to optimize care changes quickly to be used effectively.

The solution to all the problems in machine learning and deep learning can enhance every stage of patient care, from disease diagnostic to treatment or selection of therapy .this way, the clinical practice will become more effective, more convenient, more efficient, and more personalized[3]. Also, the sheer volume of healthcare data for different patients and different diseases will only be collected from the healthcare system. The biomedical data collected remotely and automatically from different sources also help physicians to monitor and interpret data from machine learning or AI methods.

Role of Artificial Intelligence in Cardiology

In today’s era, technology pervades many fields, including healthcare systems, and the future of current health systems is intertwined with the advancement of technology. The potential of Artificial intelligence-based applications in the health system has played a role in better patient care and fast disease treatment processes.[4]. The terms Machine learning (ML), Artificial intelligence (AI), and deep learning (DL) are used interchangeably. This paper discusses each technique one by one. Artificial intelligence refers to using many problem-solving techniques or algorithms to give machines the ability to think better and solve real-time problems effectively by making efficient decision-making.[5] Machine learning achieves AI through methods that include unsupervised learning, supervised learning, and reinforcement learning.

Machine learning

A subfield of AI, consists of various techniques for solving complicated problems related to big data by identifying patterns in data .it used techniques that allow the machines to learn from unstructured data, identify patterns amount data, and then use these patterns for decision making. The subfield of AI, ML, encompasses various techniques for solving complicated problems with big data by identifying interaction patterns among variables.[6] It uses software that allows computers to learn from data, identify patterns and make decisions.

One example of ML in cardiology is the prediction of heart disease from data. The healthcare systems keep a record of the previous history of patients of a particular disease, so by closely analyzing the patient’s previous history, patients current activity and eating habits records as well as certain other factors, the machine learning algorithms can determine the health state of a patient by telling how the patients can avoid heart attacks by not following the patterns and eating habits that the previous patients of heart diseases followed. [7]By accurate predictions the doctors can suggest advance precaution as well as choose the treatment method based on the prediction. The prediction also helps in choosing a method for therapy and surgery. Machine learning Algorithms have three learning types, namely supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning

It uses labeled data (the data that have been named for each particular class).In supervised learning, the training algorithms are given both the input data and the correct responses for learning. It is used in regression and classification problems and classifies future events.[8] the data must be labeled correctly because incorrect labeling of data may result in a less effective model since it does not determine the correct input-output link due to wrong labeling o the data. Electrocardiogram (ECG) measures the electrical activity of the heart. It is used to investigate some types of abnormal heart function.

Reinforcement learning

is the combination of supervised and unsupervised learning. In this method, the model learns from the environment .it is somehow a feedback loop system in which the model receives a reward if it performs correctly and is punished if it gives wrong results .so the model learns without any human intervention by maximizing the reward and minimizes the penalty. And, In unsupervised learning, as the name reveals, the user does not supervise the machine. In unsupervised learning, unlabeled data is given to the model. The model is expected to highlight undetected patterns in the data .so the data in unsupervised learning is neither classified nor labeled. Unsupervised learning sometimes performs very well because it finds all kinds of unknown patterns in data and finds features that can be very useful for classification. Also, it is easier to get unlabeled data than labeled data.

For example, machine learning has played a role in coronary artery disease detection. In coronary artery disease, plaque accumulates in the arteries, which reduces the blood flow or sometimes completely blocks the arteries, resulting in a heart attack. So machine learning algorithms use parameters like vessel diameter and area expansion ratio for early detection of disease.[9]

Deep learning

is a sub-field of machine learning concerned with algorithms inspired by the human brain that use multiple layers to extract useful features from input data. In cardiology, deep learning algorithms are applied to cardiac imaging modalities to perform the segmentation and classification of the different heart regions. [10]accurate segmentation and classification of cardiac images help to diagnose various cardiovascular diseases. Convolutional neural networks are a type of deep learning-based algorithms consisting of multiple layers and detecting different image features.


Various artificial intelligence-based methods and their applications in the field of cardiology have been discussed here. This shows that how the field of artificial intelligence has revolutionized the process of detection of cardiac disease. Various machine and deep learning methods have shown promising results in cardiac function assessment and early diagnosis of cardiac disease. Deep learning and machine learning methods have really changed and improved the treatment methods in the medical field, especially cardiology. In the coming era, with the invention of more powerful AI algorithms, healthcare professionals can cooperate with IT experts to reach a common point in discussing and resolving the problems in using AI in cardiology. This way of practicing medicine is auspicious, and it will definitely develop even more in the upcoming years.

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