Cardiac Death: American researchers have created a new Artificial Intelligence (AI) model that is much better than existing medical guidelines in identifying high -risk patients of sudden cardiac death (sudden death due to cardiovascular stay). The name of this AI model is ‘Multimodal AI for Ventricular Aridamia Risk Stratization’. According to the report by the news agency Xinhua, this model detects hidden warnings by mixing cardiac MRI photos and health records of patients. This technique provides much more accuracy in estimating the risk of heart related diseases.
Causes of sudden cardiac death
The study published in the journal ‘Nature Cardiovascular Research’ paid attention to hypertrophic cardiomyopathy, which is a common genetic heart disease and a major cause of sudden cardiac death among the youth.
Technology will tell which patients threatened
Natalia Tryanova, researcher at Johns Hopkins University, said, “Many patients are dying due to this disease in youth, because they are not able to be treated at the right time. At the same time, some people are unnecessarily living with the defibrilator. Our AI model can tell with 89 percent accurately, which patients are more in danger of death which patients are suddenly threatened.”
What signs are made the reason for cardiac death
The accuracy to identify at risk patients in the existing medical guidelines used in the US and Europe is only estimated at 50 percent. In comparison, the ‘Mars’ model showed 89 percent accuracy. This accuracy was up to 93 percent, especially for patients between 40 to 60 years, who are at the highest risk.
This model analyzes the contrast-eneensed MRI scan and understands the pattern of wounds in the heart, which was difficult for doctors to understand. Through deep learning technology, this model recognizes the major signs that can cause sudden cardiac death.
Johns Hopkins’ cardiologist and co-author Jonathan Crispin said, “This AI model makes the risk predicted compared to the current algorithm and can change the medical field.”
Researchers are now planning to try this model at more patients and use it for other cardiovascular diseases, such as cardiac sarcoidosis and arithmogenic right ventricular cardiomyopathy.
