Introduction
The term cardiovascular disease describes the disease of the heart and blood vessels. According to the World Health Organization, 17.9 million people died due to cardiovascular diseases. It is the leading cause of death globally, and the burden has increased. Early diagnosis and proper treatment can manage the disease and reduce the death rate. People with a higher risk of heart disease need early, quick preventive actions to reduce mortality.
What Is Artificial Intelligence?
Artificial intelligence (AI) is a computer system used to perform various tasks performed with human intelligence. AI uses algorithms, pattern learning, rules, deep learning, and cognitive computing to give results. The use of AI helped to achieve accurate and fast results, which otherwise take time for a human to do.
The development of AI techniques and the subdomains of machine learning (ML) and deep learning (DL) help clinicians create reliable and effective ways of delivering quality health care. Machine learning is a subdomain of artificial intelligence used to teach computers to analyze a large data set quickly and accurately. Machine learning identifies patterns in the uploaded data matches them with the existing data they have already learned, and makes predictions based on that. Deep learning is a supervised machine learning technique that helps computers process data in a base similar to the human brain.
How Is Artificial Intelligence Being Used in Cardiology?
Artificial intelligence uses various algorithms in medical activities like diagnosis, risk assessment, selection of treatment procedures, and prevention.
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Diagnosis: Artificial intelligence helps in the early diagnosis of potential cardiac abnormalities, which greatly helps the cardiologist to begin with the treatment. It detects problems early and identifies people at high risk of developing cardiovascular diseases shortly. Stanford University has formulated an AI algorithm that evaluates the speed of blood pumped by the heart and diagnoses cardiac issues. Another formal letter algorithm assesses echocardiograms as done by a highly qualified cardiologist and diagnoses quickly.
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Heart Monitoring: Artificial intelligence made it possible to self-monitor heart diseases and to detect the signs of severe cardiac arrest or heart failure. The deep learning algorithm systems can analyze the echocardiography accordingly and predict the risk of possible heart failure in the future. It also helps in
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Prediction of cardiac history of the patient.
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Detection of arrhythmia or irregular heartbeat.
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Differentiate between arrhythmias and a normal heartbeat.
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Predict the risk of chronic heart failure.
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Selection of Treatment: Artificial intelligence uses various algorithms to recommend the best treatments based on the patient's clinical condition. AI also helps in treatment selection for patients who have heart attacks or narrowed arteries. Below is the list of appropriate treatments that AI will predict,
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Blood thinning drugs.
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Blood pressure medication.
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Heart surgery.
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Artificial valve replacement.
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Cardiac Risk Prediction: Cardiac risk assessment is a vital task for cardiologists. Doctors use real-time data and a patient's medical history to predict risk for various cardiovascular diseases. It is suggested that artificial intelligence can predict the 10-year risk of stroke by evaluating a patient's blood pressure, heart rate, and pulse. The clinical studies have utilized the data of 859 patients for cardiac risk prediction, and the results were accurate as per the judgment of a trained cardiologist.
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Detection of Cardiac Events and Monitoring: The major advantage of artificial intelligence is to analyze the data and make proper decisions at a fast pace. Machine learning algorithms have a high degree of accuracy and help doctors to detect adverse events. It can also detect and monitor various cardiac events in real time. Even mobile applications provide real-time data enabling physicians to work remotely and provide better care.
What Are the Applications of AI in Cardiovascular Imaging?
Artificial intelligence helps to generate knowledge in the data and to make decisions in cardiovascular imaging.
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Echocardiography: Echocardiography helps in the accurate assessment of cardiac structures and functions. The AI tools helped in the interpretation of ECG in an automated manner. It also reduces the analysis time, and reproducibility has increased. The model-based algorithm helps in the 3D echocardiographic model analysis. All the major cardiovascular diseases can benefit from the machine learning models.
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Magnetic Resonance Imaging (MRI): Machine learning models have applications in the field of ventricular segmentation. It helps to improve the efficiency and reproducibility of the clinical assessment. In cardiac MRI, machine learning helps in the detection of subacute myocardial scars (partially healed areas of damage in the heart tissue).
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Cardiac Computed Tomography: In cardiac computed tomography, the machine learning system helps in the following.
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Diagnosis and risk assessment of coronary artery disease.
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Diagnosis of atherosclerosis.
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Myocardial infarction detection and prognosis.
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Coronary artery disease diagnosis upon the three-dimensional construction of the coronary arteries.
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The coronary artery disease predictions by assessing complex plaque growth computational models.
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Electrocardiography:
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Electrocardiogram is widely used to identify abnormalities in the electrical activity of the heart. Artificial intelligence helps in the automatic detection of the ECG, reducing the time of interpretation and also reducing the variability based on individual assessment.
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Artificial intelligence also helps in the identification and classification of ECG phenotypes to find the arrhythmic risk markers in hypertrophic cardiomyopathy (a heart condition characterized by abnormal thickening of the heart muscle, potentially leading to impaired function). Also, it helps to identify and classify 12 types of different arrhythmia.
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How Artificial Intelligence Can Be Integrated Into Cardiovascular Care?
Artificial intelligence can be incorporated into cardiovascular patient care through the following methods.
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Enhanced Stroke Diagnosis:
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AI is implemented in emergency rooms for intracerebral hemorrhage stroke ( bleeding within the brain tissue, leading to damage and potential impairment of neurological functions) cases.
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Computer-trained analysis of computed tomography (CT) scans gives diagnosis, reducing time and minimizing brain damage.
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Affordable Heart Pump Detection:
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Application of AI to electrocardiogram (ECGs) for a cost-effective test identifying a weak heart pump.
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Access to a vast ECG database facilitates swift and accurate predictions of potential heart failure.
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Early Detection of Atrial Fibrillation (AFib):
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AI-guided ECGs are employed to identify atrial fibrillation (AFib) before symptom manifestation.
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Enables early detection of faulty heart rhythms, improving the chances of timely intervention.
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What Are the Challenges of AI in Cardiology?
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Medical ethical concerns, where the AI systems do not have an emotional basis and ethics.
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Artificial Intelligence can diagnose but requires professional cleaning for optimal patient care.
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The development of high-quality databases that are clinically relevant takes more time and effort from skilled professionals.
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The diagnosis in AI systems is made from images, but it does not have adequate information to understand normal variations in clinical presentations.
Conclusion
Artificial intelligence is transforming the field of cardiology in terms of early diagnosis and proper treatment. It opens new ways for early detection and risk assessment for various cardiovascular diseases. The real-time monitoring of cardiac events, even in their homes without the need for a physician, is a revolutionary change. Early diagnosis, risk assessment, and prevention will help to reduce the morbidity and mortality associated with heart diseases. It also modified how cardiologists select the treatment for the patients.
