Artificial Intelligence (AI) has revolutionized the healthcare industry in recent years. With the help of machine learning algorithms and predictive analytics, AI has been successful in predicting various diseases. One of the most common diseases that AI can predict is heart disease. When AI can detect or predict heart disease, loved ones of those at high risk can consider becoming trained in CPR and keeping an AED nearby, potentially increasing the likelihood of saving a life in the event of a sudden cardiac arrest.
Heart disease is a leading cause of death globally, with millions of people affected annually. Traditional methods for detecting heart disease include physical examination, blood tests, and electrocardiogram (ECG). However, these methods can be time-consuming, and their accuracy depends on the expertise of the medical professional.
Artificial Intelligence, on the other hand, can analyze large amounts of medical data, including medical histories, symptoms, and test results, to detect patterns and identify potential risks for heart disease. Machine learning algorithms can learn from this data and improve their accuracy over time, making them more reliable than traditional methods.
AI has already been used in various studies to predict heart disease. In one study, researchers used machine learning algorithms to analyze the medical records of over 400,000 patients. The algorithm was able to predict heart disease in patients with an accuracy of 72.8%. Another study used machine learning to analyze ECG data to predict heart disease with an accuracy of 85.4%. These studies demonstrate the potential of AI in predicting heart disease.
Artificial Intelligence can also be used to develop personalized treatment plans for patients with heart disease. By analyzing patient data, AI can determine the most effective treatment plan for each individual patient, improving their chances of recovery.
Predicting Sudden Cardiac Arrest
Sumeet Chugh, MD, Cedars Sinai professor and Chief of Clinical Electrophysiology Cardiology, and his team have developed a novel scoring system that assesses a person’s risk of ventricular fibrillation, a major cause of sudden cardiac arrest. By analyzing large collections of clinical data with AI tools, they are able to validate and improve their ability to predict who is at risk of a fatal cardiac arrest.
Imaging Insight
According to an article on the Cedars Sinai website, Damini Dey, PhD, a professor of Biomedical Sciences and a scientist, along with her team of investigators, has developed an AI-based tool that utilizes a standard CT test to measure plaque buildup in the coronary arteries. They have also cross-referenced the results with images obtained from two invasive tests, considered to be highly accurate in assessing coronary artery plaque and narrowing – intravascular ultrasound and catheter-based coronary angiography. Through a multicenter trial, the investigators discovered that measurements obtained from the CT angiography images by the AI algorithm accurately predicted heart attack risk within five years. The team has also utilized AI to combine the measured image parameters from the standard CT test to identify patients who are at higher risk of coronary blockages. This enables doctors to recommend interventions that can prevent chest pains and heart attacks.
Current Prediction is Only As Good as the Data
Despite the benefits of AI in predicting heart disease, there are also some challenges to consider. One of the major challenges is the quality and quantity of medical data. AI algorithms require a large amount of high-quality data to improve their accuracy. However, medical data can be difficult to obtain, and there are privacy concerns surrounding the use of patient data.
With the latest advancements in the last year, the prediction of heart disease has advanced tremendously. Within the next five years, the prediction of heart disease will be available to help doctors identify, treat and prevent more cardiac arrests and heart attacks.
Artificial Intelligence has the potential to revolutionize the way we predict and treat heart disease. With its ability to analyze large amounts of medical data and learn from it, AI can predict heart disease with high accuracy and develop personalized treatment plans for patients. However, there are also challenges that need to be addressed, such as the quality and quantity of medical. With further research and development, AI can play a crucial role in improving patient outcomes and reducing the impact of heart disease.
Sources:
- “Prediction of Cardiovascular Disease by Machine Learning: A Systematic Review and Meta-analysis” published in the Journal of the American College of Cardiology, 2019.
- “Deep ECGNet: An Efficient Deep Learning Model for Detecting Cardiac Abnormalities,” published in the IEEE Journal of Biomedical and Health Informatics, 2020.
- “A New Partner In Heart Disease Prediction: AI” published on the Cedars-Sinai.org website, February 22, 2023
