HospiMedica

Download Mobile App
Recent News AI Critical Care Surgical Techniques Patient Care Health IT Point of Care Business Focus

AI Technology Boosts ECG Capabilities for Early Heart Disease Diagnosis

By HospiMedica International staff writers
Posted on 29 Feb 2024
Print article
Image: Detecting heart diseases using AI and the ECG (Photo courtesy of 123RF)
Image: Detecting heart diseases using AI and the ECG (Photo courtesy of 123RF)

Cardiovascular diseases often remain undetected until a critical event like a heart attack or stroke occurs. Early identification is key to improving outcomes, but the absence of clear symptoms complicates this process. Advances in artificial intelligence (AI) technology are now enhancing the capabilities of the electrocardiogram (ECG), a century-old diagnostic tool, potentially enabling earlier detection and monitoring of heart diseases. Researchers at Mayo Clinic (Rochester, MN, USA) have pioneered the development of ECG-AI algorithms. These algorithms are currently used in research settings to assess the likelihood of various heart conditions, such as atrial fibrillation, amyloidosis, aortic stenosis, low ejection fraction, and hypertrophic cardiomyopathy (HCM). They also use AI to estimate a patient's biological age from both traditional 12-lead ECGs and single-lead ECGs obtained from smartwatches and other portable devices.

The ECG-AI is particularly effective in detecting low ejection fraction, a condition where the heart weakens and pumps less blood. Often, the symptoms of this condition are overlooked or attributed to other causes, like pregnancy, leading to a late diagnosis. ECG-AI can also identify peripartum cardiomyopathy, a type of heart muscle weakness that occurs during or after pregnancy. Similarly, it can help in the early detection of amyloidosis, a rare disease characterized by the buildup of misfolded proteins in organs, which can lead to heart failure.

HCM, a common genetic heart disease, is another condition where ECG-AI proves beneficial. HCM often goes unnoticed as it may not be evident in basic tests like a traditional ECG. However, ECG-AI can identify HCM by detecting patterns that might be missed even by expert clinicians. The potential of ECG-AI to spot these subtle indications early can be crucial in improving patient outcomes and treatment strategies for various heart conditions.

Related Links:
Mayo Clinic

Gold Member
12-Channel ECG
CM1200B
Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
New
Antimicrobial Barrier Dressing
ACTICOAT FLEX
New
Wound Care Cart
UMGKA-3669-LTG

Print article

Channels

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more