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Artificial Intelligence in Cardiovascular Diseases - Applications, Benefits, and Challenges

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Artificial Intelligence is effective in predicting cardiac diseases. Read the article below to learn about the applications of AI in Cardiovascular disease.

Medically reviewed by

Dr. Muhammad Zohaib Siddiq

Published At February 3, 2023
Reviewed AtJuly 4, 2023

Introduction:

Cardiovascular diseases cause a major number of deaths globally. The rapid economic development has led to drastic changes in lifestyle in addition to the many environmental risk factors contributing to heart diseases. Unfortunately, there is a yearly increase in the number of people diagnosed with cardiovascular disease. To overcome this situation, scientists have introduced AI (artificial intelligence) to improve the strategy for diagnosing and treating heart diseases. This article describes the prediction of heart diseases using artificial intelligence. AI's goal is to overcome shortcomings of the current medical systems like:

  • Misdiagnosis by primary clinicians.

  • High medical expenses.

  • Long training time for doctors.

  • Uneven distribution of senior physicians or clinicians.

  • Shortage of physicians in undeveloped areas.

What Is Artificial Intelligence?

A field in computer science, artificial intelligence constructs machines that replicate or simulate human intelligence. The main aim of artificial intelligence is to replace human intelligence to accomplish critical tasks on par with humans. Physicians who know about artificial intelligence can evaluate how to improve their work with AI, which will benefit them since AI can assist them in some necessary procedures. A branch of artificial intelligence, machine learning, permits a machine to analyze and arrive at a solution through in-depth analysis of historical data. Machine learning also includes deep learning. Deep learning is a method that enables the algorithm to program automatically and learn from big data. Artificial intelligence (AI) has developed and advanced various fields, such as:

  • Banking and financial markets.

  • Education.

  • Supply chains.

  • Manufacturing.

  • Retail and e-commerce.

  • Healthcare.

  • Drug research.

  • New drug development.

AI can help clinicians make accurate patient predictions through machine learning and big data analytics. Artificial intelligence in healthcare has a pattern. It starts with a large amount of data; machine-learning algorithms are employed to gain information, which is then used to solve problems in the medical system. AI is used in healthcare at the initial stages for remote follow-ups, medicine reminders, disease counseling, and warnings of disease symptoms. AI enhances patient care and patient experience and provides support to doctors via AI assistants; AI applications in medical sciences include;

  • Patient diagnosis and prognosis.

  • Correlating patient symptoms to appropriate physicians.

  • Drug discovery.

  • Transcribing medical information.

  • Organizing files and diagnostic images.

  • More reliable images and diagnostic information.

  • Finding physicians who are on call.

  • Scheduling the next appointment.

  • Mobile applications for a list of drugs and medical devices thus improve workflow in the hospital.

  • AI robots can improve the efficiency of surgical procedures.

  • AI can deal with large data sets that are difficult to analyze by the traditional data processing system. AI has become an important factor in improving medical services.

  • Optimization of treatment processes.

What Are the Applications of AI in Cardiovascular Diseases?

The risk factors that are used as data for cardiovascular diseases are:

  • Diabetes.

  • Smoking.

  • Obesity.

  • Dyslipidemia.

  • Lifestyle.

  • High serum in the blood.

  • CABG (coronary artery bypass surgery).

  • Hypertension.

The most crucial step in diagnosis is scans and X-rays. AI prevents exposure of the normal human body to CT (computed tomography) scan's radioactive rays because the human body is prone to cancer on exposure to high radiation. The basic concept behind the AI system is a "neural network." A computer system is trained by analyzing hundreds of thousands of similar readings. The result is that an AI system can find out what a simple test is, detect a heart condition, and predict future problems.

  • Prediction of risk in conditions such as embolic stroke.

  • Monitoring the heart.

  • Detection of arrhythmia.

  • Developing AI technology adaptable with smartphones and high-tech stethoscopes.

  • With deep learning, cardiac imaging analysis has shown significant development.

  • Deep understanding can help to analyze coronary angiography, echocardiography, and electrocardiogram (ECG).

  • Cardiac intervention has been the primary treatment for cardiovascular disease in recent years, including CHD (Coronary Heart Disease) and acute coronary syndrome (ACS). Deep learning allows AI to identify coronary atherosclerotic plaques more precisely than clinicians.

  • In addition, AI is used to analyze echocardiographic images, including assessment of left ventricular function and automatic measurement of the size of each chamber.

  • It is used to assess diseases, such as valvular disease, and determine the classification and staging of the disease.

  • Artificial Intelligence's newer diagnostic system predicts the onset of cardiovascular disease. It could provide a new tool for doctors looking to diagnose disease earlier in patients, thereby reducing the symptoms and tragic outcomes.

  • Voice information of the patients can be collected and matched with the electronic health records (EHR), which helps reduce clinicians' workload.

  • Research studies suggest that AI can predict the possible time of death for heart disease patients.

  • The AI software recorded the results of cardiac magnetic resonance imaging (MRI) scans and blood tests of heart disease patients. The software measured the movement of many points on the heart structures in each heartbeat. By integrating these data with the patients' eight-year health records, AI could predict the conditions leading to a patient's death. Furthermore, their software could predict patients' survival rates for the next few years.

  • In addition, few studies have established a predictive model using deep learning to evaluate the risk of death for suspected coronary heart disease (CHD) patients for the next five years. Their results indicated that the risk assessment based on AI is higher than traditional clinical judgment and coronary computed tomographic angiography.

  • AI in cardiac imaging analysis includes an intravascular ultrasound (for the detection of the border of the lumen and the media-adventitia).

  • Optical coherence tomography (for classification of three layers of the coronary artery).

  • Cardiac single-photon emission computed tomography (for diagnosing myocardial ischemia and for diagnostic accuracy of myocardial perfusion imaging).

  • MRI (for efficient and fast visualization of the cardiac segmentation in short-axis MRI).

  • AI can perform cardiac interventional operations like percutaneous coronary intervention (PCI) operations that reduce radiation exposure to clinicians by using digital subtraction angiography.

What Are the Shortcomings of AI?

  • High production cost.

  • Risk of unemployment.

  • Lack of creativity.

  • Implementation of machine learning requires lots of datasets that are not easily accessible.

  • With good data, results will be accurate, and bias can occur.

  • Therefore, acceptance and implementation is a long process.

Conclusion:

AI can help deal with rapidly increasing global cardiovascular problems. With the right clinical knowledge and skills, physicians can successfully treat with the help of AI. However, AI cannot completely replace humans; instead, it can fasten the process.

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Dr. Muhammad Zohaib Siddiq
Dr. Muhammad Zohaib Siddiq

Cardiology

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artificial intelligence (ai)cardiovascular disease risk
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