More Accurate Test Readings
A coronary CT scan is performed to assess a patient’s risk for heart disease. The Coronary Artery Disease and Reporting Data System (CAD-RADS) is used to assess test imaging but only focuses on certain factors. A machine learning system has been developed to assess multiple CT imaging details for better determining heart disease risk. The AI system proved more accurate than CAD-RADS at assessing disease risk and determining which patients should receive medication.
Poor blood flow is a risk factor in heart attack and stroke, but noninvasive imaging to measure blood flow is difficult to read. Researchers at University College London used artificial intelligence to analyze cardiovascular magnetic resonance (CMR) imaging to assess blood flow. The AI was able to quickly and precisely quantify blood flow and predict which patients were likely to experience future heart complications.
An electrocardiogram (ECG) is commonly performed to test heart function in emergency room patients. The test can identify cardiac abnormalities but not failure. Blood tests can be used to identify markers of cardiac failure but are unreliable. To help with diagnosis, Mayo Clinic researchers taught artificial intelligence to distinguish between ECG patterns of people ultimately diagnosed with heart disfunction and those who were not. The AI enhanced ECG performed better and faster at diagnosing disfunction than standard blood tests.
Identifying New Risk Markers
Researchers used AI to identify a new biomarker for heart attack. Researchers first used fat biopsies from patients undergoing cardiac surgery to analyze genes associated with inflammation, scarring and blood vessel formation. The expression of these genes was matched with coronary CT scan images to determine features indicating changes to the fat surrounding the heart. Researchers then used AI to develop the fat radiomic profile (FRP), which identifies red flags in the fat lining the blood supply to the heart. This new profile could be used to predict heart attacks in patients up to five years before they occur.
Wearables for Monitoring
An AI wearable can help prevent up to a third of heart failure repeat hospital admissions. The device is worn by heart failure patients for up to three months after being discharged from the hospital and performs continuous ECGs. The AI establishes normal heart rate, heart rhythm, respiratory rate and walking and sleep patterns for each individual patient. It then analyses deviations from these norms for indications the patient’s heart condition is worsening. The system was able to predict rehospitalization with 85% specificity up to 10 days before patients were readmitted.
An AI hospital monitoring device has received approval for home use by patients with chronic obstructive pulmonary disease (COPD) and heart failure. The device monitors pulse, respiration, oxygen saturation, temperature and mobility. The data is analyzed for warning signs and health care providers are automatically alerted.
A personal AI device is available for detecting atrial fibrillation (or AFib). The device is worn around the chest and performs continuous ECGs. AI interprets the data and alerts wearers to irregularities via mobile app. Individuals have control of their own data and choose when to share it with their doctor.
Research shows patients who wear monitoring devices are more likely to receive a timely diagnosis than those who receive standard care.