Artificial Intelligence Screening of Cardiovascular Risk in COVID-19 Patients- A 10 Step Screening Program
Authors: Amina Khalpey, PhD, Brynne Rozell, BS, Zain Khalpey, MD, PhD, FACS
Cardiovascular Disease and COVID-19
Cardiovascular disease is one of the leading causes of death in the United States and worldwide. In the United States, it is the leading cause of death for both men and women. It is estimated that about 610,000 people in the U.S. die from cardiovascular disease each year, which is about 1 in every 4 deaths.
The COVID-19 pandemic has highlighted the need for improved methods of predicting and detecting cardiovascular disease. In addition to the respiratory symptoms associated with the virus, COVID-19 has also been shown to cause cardiovascular complications, including myocarditis, arrhythmias, and thromboembolisms.
Machine Learning Analyzes Lots of Data
Electronic medical records (EMRs), echocardiograms, electrocardiograms (EKGs), and chest CT scans are all commonly used in the detection and diagnosis of cardiovascular disease. The quickly developing artificial intelligence technology called machine learning (ML) algorithms have the potential to enhance the accuracy and efficiency of these diagnostic tools in predicting cardiovascular disease related to COVID-19.
ML algorithms can be trained on large amounts of data from EMRs to identify patterns and relationships that may not be apparent to individual healthcare providers. For example, the algorithms can analyze a large demographic information, such as age, gender, and ethnicity, as well as medical history information, such as a history of cardiovascular disease, to predict the risk of cardiovascular complications in COVID-19 patients.
Echocardiograms and EKGs Can Be Analyzed With Machine Learning
Echocardiograms, which use ultrasound to produce images of the heart, can also be analyzed using ML algorithms to predict the risk of cardiovascular complications in COVID-19 patients. These algorithms can be trained to recognize and identify specific features in echocardiogram images that are indicative of cardiovascular disease, such as the presence of blood clots or inflammation in the heart and blood vessels. 
EKGs, which measure the electrical activity of the heart, can also be analyzed using ML algorithms to predict the risk of cardiovascular complications in COVID-19 patients. These algorithms can be trained to identify specific patterns in the EKG data that are indicative of cardiovascular disease, such as arrhythmias or changes in heart rate.
Machine Learning Can Analyze Imaging to Augment Decision Making
Chest CT scans, which produce detailed images of the chest and surrounding structures, can also be analyzed using ML algorithms to predict the risk of cardiovascular complications in COVID-19 patients. These algorithms can be trained to identify specific features in chest CT images that are indicative of cardiovascular disease, such as the presence of blood clots or inflammation in the heart and blood vessels.
Machine Learning Enhances Accuracy and Efficiency
In conclusion, ML algorithms have the potential to significantly enhance the accuracy and efficiency of commonly used diagnostic tools in the detection and diagnosis of cardiovascular disease related to COVID-19. By analyzing large amounts of data compiled together from EMRs, echocardiograms, EKGs, and chest CT scans, these algorithms can help healthcare providers to quickly identify patients who are at high risk of cardiovascular complications and provide early intervention for improved outcomes.
A 10-Step Screening Program to Predict Cardiovascular Complications in COVID-19 Patients:
Step 1: Review Electronic Medical Records (EMRs) In this step, healthcare providers would review the patient’s electronic medical record to gather demographic information, such as age, gender, and ethnicity, as well as medical history information, such as a history of cardiovascular disease.
Step 2: Evaluate Symptomology The healthcare provider would also assess the patient’s symptoms to determine if they are presenting with any cardiovascular symptoms, such as chest pain, shortness of breath, or palpitations.
Step 3: Perform an Echocardiogram An echocardiogram, which uses ultrasound to produce images of the heart, would be performed to assess the patient’s cardiac function.
Step 4: Analyze Echocardiogram using Machine Learning (ML) Algorithms The echocardiogram images would then be analyzed using ML algorithms to identify specific features that are indicative of cardiovascular disease, such as the presence of blood clots or inflammation in the heart and blood vessels.
Step 5: Perform an Electrocardiogram (EKG) An electrocardiogram, which measures the electrical activity of the heart, would also be performed to assess the patient’s heart rhythm and identify any arrhythmias.
Step 6: Analyze EKG using ML Algorithms The EKG data would then be analyzed using ML algorithms to identify specific patterns that are indicative of cardiovascular disease, such as arrhythmias or changes in heart rate.
Step 7: Perform a Chest CT Scan A chest CT scan, which produces detailed images of the chest and surrounding structures, would also be performed to assess the patient’s cardiovascular system.
Step 8: Analyze Chest CT Scan using ML Algorithms The chest CT images would then be analyzed using ML algorithms to identify specific features that are indicative of cardiovascular disease, such as the presence of blood clots or inflammation in the heart and blood vessels.
Step 9: Integration of Results The results from the EMR review, echocardiogram analysis, EKG analysis, and chest CT scan analysis would be integrated to provide a comprehensive assessment of the patient’s cardiovascular system and risk of cardiovascular disease.
Step 10: Risk Assessment and Follow-Up Care Based on the results of the screening program, the healthcare provider would provide a risk assessment for the patient and develop a follow-up care plan, including any necessary interventions, to prevent or manage the development of cardiovascular disease.
In conclusion, a 10-step screening program that incorporates EMRs, echocardiograms, EKGs, and chest CT scans, as well as ML algorithms, can help healthcare providers to quickly and accurately predict the risk of cardiovascular disease in COVID-19 patients. This program would provide a comprehensive assessment of the patient’s cardiovascular system and enable early intervention to prevent or manage the development of cardiovascular disease.
