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AI-Enhanced ECGs Can Improve Diagnosis and Treatment of Obstructive Hypertrophic Cardiomyopathy

By HospiMedica International staff writers
Posted on 09 Mar 2022
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Image: AI-ECG can identify early hypertrophic cardiomyopathy (Photo courtesy of UCSF)
Image: AI-ECG can identify early hypertrophic cardiomyopathy (Photo courtesy of UCSF)

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 


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