AI-Enhanced ECGs Can Improve Diagnosis and Treatment of Obstructive Hypertrophic Cardiomyopathy
By HospiMedica International staff writers Posted on 09 Mar 2022 |
Using artificial intelligence (AI) in electrocardiogram (ECG) analysis can improve diagnosis and treatment of hypertrophic cardiomyopathy (HCM), according to findings of a new study pointing to the potential benefits for remote monitoring of the condition.
The study by researchers at the University of California San Francisco (UCSF, San Francisco, CA, USA) found that AI-ECG may help identify HCM in its earliest stages and monitor important disease-related changes over time. The team demonstrated that AI analysis of ECGs can not only accurately predict the diagnosis of HCM, but also that AI-ECG correlates longitudinally with cardiac pressures and lab measurements related to HCM. The study showed that AI analysis can capture far more information from ECGs related to obstructive HCM pathophysiology than is currently gained by manual ECG interpretation and was the first study to show that AI analysis of ECGs can potentially be used to monitor disease-related physiologic and hemodynamic measurements.
The researchers applied two separate AI-ECG algorithms to pre-treatment and on-treatment ECGs from the phase-2 PIONEER- OLE clinical trial (a clinical trial for treatment with the HCM drug Mavacamten in adults with symptomatic obstructive HCM). After showing that both algorithms accurately detected HCM in clinical trial data without additional training, they then showed that AI-ECG HCM scores correlated longitudinally with disease status as measured by decreases over time in left ventricular outflow tract gradients and natriuretic peptide (NT-proBNP) levels in these patients.
The longitudinal associations of the AI-ECG HCM score were significant and likely reflected changes in the raw ECG waveform that were detectable by AI-ECGs and correlated with HCM disease pathophysiology and severity. AI-ECG’s potential is broadened by the fact that ECGs can now be measured remotely via smartphone-enabled electrodes and may permit remote assessment of disease progression as well as drug treatment response. According to the researchers, future studies are needed to determine whether AI-ECGs can track disease status and be used as a guide for drug measurement to enhance safety.
Related Links:
University of California San Francisco
Latest Patient Care News
- First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds
- Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization
- Game-Changing Innovation in Surgical Instrument Sterilization Significantly Improves OR Throughput
- Next Gen ICU Bed to Help Address Complex Critical Care Needs
- Groundbreaking AI-Powered UV-C Disinfection Technology Redefines Infection Control Landscape
- Clean Hospitals Can Reduce Antibiotic Resistance, Save Lives
- Smart Hospital Beds Improve Accuracy of Medical Diagnosis
- New Fast Endoscope Drying System Improves Productivity and Traceability
- World’s First Automated Endoscope Cleaner Fights Antimicrobial Resistance
- Portable High-Capacity Digital Stretcher Scales Provide Precision Weighing for Patients in ER
- Portable Clinical Scale with Remote Indicator Allows for Flexible Patient Weighing Use
- Innovative and Highly Customizable Medical Carts Offer Unlimited Configuration Possibilities
- Biomolecular Wound Healing Film Adheres to Sensitive Tissue and Releases Active Ingredients
- Wearable Health Tech Could Measure Gases Released From Skin to Monitor Metabolic Diseases
- Wearable Cardioverter Defibrillator System Protects Patients at Risk of Sudden Cardiac Arrest
- World's First AI-Ready Infrasound Stethoscope Listens to Bodily Sounds Not Audible to Human Ear