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Discover how artificial intelligence is helping diagnose and treat cardiometabolic diseases, improving prevention and long-term prognoses.
Cardiometabolic diseases, which include conditions such as cardiovascular diseases, type 2 diabetes, hypertension, and obesity, are among the leading causes of death globally. Despite medical advancements, the prevalence of these diseases continues to rise, largely due to factors like obesity, limitations of modern medicine, and inequalities in access to healthcare. In this context, artificial intelligence (AI) is emerging as a key tool to improve early diagnosis, risk stratification, and long-term prognoses. In this article, we will explore how AI is transforming the fight against cardiometabolic diseases.
Cardiometabolic diseases are responsible for millions of deaths each year. It is estimated that 1 in 3 adults has 3 or more risk factors contributing to these diseases, such as obesity, hypertension, and diabetes. In the United States, metabolic diseases account for 20% of hospitalizations and 4.8 million deaths annually. Globally, the situation is equally concerning, with over 70% of adults being overweight, one of the most relevant risk factors for these conditions.
The lack of early diagnosis and proper access to treatment remains one of the main barriers to combating these diseases. However, technology is advancing rapidly, and AI is playing a crucial role in improving how we identify, treat, and prevent these conditions.
Improving Prediction of Metabolic Syndrome
One of the greatest challenges in the fight against cardiometabolic diseases is early diagnosis. Metabolic syndrome, which includes a series of risk factors such as hypertension, abdominal obesity, and insulin resistance, significantly increases the risk of cardiovascular diseases and type 2 diabetes. However, traditional diagnostic methods, such as blood tests and weight measurements, do not always adequately detect the risk.
A significant advancement in this field is the use of 3D body volume scanners trained with AI. Devices like the one developed by the Mayo Clinic can accurately measure body fat and its distribution, critical factors for assessing the risk of metabolic diseases. The 3D scanner uses AI to analyze this data, improving risk prediction by up to 20% compared to traditional methods. With this approach, it is possible to detect the risk of metabolic syndrome much earlier, allowing for earlier and more effective interventions.
Advances in Coronary Artery Evaluation
Cardiovascular diseases, which include issues like arterial blockages, are one of the major complications associated with cardiometabolic diseases. Angioplasties and other invasive procedures are commonly used to treat blocked arteries, but these methods present risks due to the lack of precision in traditional evaluations. This is where AI comes into play.
A team of researchers has developed an AI model called Angio-FFR, which uses angiographic images to calculate fractional flow reserve (FFR) in coronary arteries. This model can analyze images with great precision and determine the severity of blockages without the need for invasive catheterizations. In one study, this model showed 92% accuracy and reduced the use of invasive procedures by 30%. This breakthrough has the potential to significantly improve diagnoses and decision-making in patients with coronary diseases.
AI, therefore, not only improves diagnostic accuracy but also reduces the risks associated with invasive interventions, offering a less aggressive and more efficient approach to treating cardiovascular diseases.
Predicting Future Cardiovascular Events
One of the greatest challenges in caring for patients with cardiometabolic diseases is predicting long-term cardiovascular events such as heart attacks or strokes. Hypertension, a key factor in the development of these complications, is often difficult to monitor continuously and accurately with traditional methods. However, AI is changing this landscape.
A research team has developed an AI model that uses video images of the radial arteries to analyze changes in skin color, which are correlated with blood pressure levels. This system can predict how hypertension could affect the future cardiovascular health of patients. With a dataset of over 2,000 images, the model has shown to improve prognoses by 20%, allowing for personalized treatments and the anticipation of cardiovascular events before they occur.
This approach not only enables more accurate prediction of long-term events but also facilitates more personalized healthcare, tailored to the specific needs of each patient.
Conclusion
Cardiometabolic diseases remain one of the leading causes of death worldwide, but artificial intelligence is proving to be a powerful tool in improving the diagnosis, treatment, and prevention of these conditions. From early diagnosis of metabolic syndrome to risk stratification in coronary diseases and predicting future cardiovascular events, AI is changing the way we approach health.
Despite these advances, prevention remains key. With the help of AI, we can significantly improve early detection rates, prevent complications, and personalize treatments, leading to a better quality of life for millions of people worldwide.
