New York, July 5th (IANS) US researchers have developed a new AI (AI) model that significantly outweighs current clinical guidelines when identifying patients at high risk of sudden cardiac death.
Known as multimodal AI for ventricular arrhythmias risk stratification (MAARS), the AI system integrates cardiac MRI images with a wide range of patient health records to detect hidden warning signs to provide a new level of accuracy in cardiovascular risk prediction.
Published in the journal Nature Cardiovascular Research, this study focuses on hypertrophic cardiomyopathy. This is one of the most common hereditary heart diseases in young people and is the leading cause of sudden cardiac death.
Senior author Natalia Trejanova, a researcher focused on the use of heart disease at Johns Hopkins University, said:
“We have the ability to predict with very high accuracy whether a patient is at a very high risk of sudden cardiac death,” added Trayanova.
Clinical guidelines used in the US and Europe currently have an estimated accuracy of only 50% in identifying at-risk patients.
In contrast, the MAARS model is the group with 89% overall accuracy and 93% at the greatest risk for patients aged 40-60.
The AI model analyzes contrast-enhanced MRI scans for cardiac scar patterns. This has traditionally been difficult for doctors to interpret. By applying deep learning to this previously unused data, the model identifies key predictors of sudden cardiac death.
“Our research shows that AI models have a significant improvement in their ability to predict the most riskiest compared to current algorithms and thus have the power to transform clinical care,” said co-author Jonathan Crispin, cardiologist at Johns Hopkins.
The team will further test new models for more patients and expand new algorithms to use with other types of heart disease, such as cardiac sarcoidosis and arrhythmic right ventricular cardiomyopathy.
– Anne
RVT/
