AI has proved itself again! Cardiovascular disease can be effectively screened with the intestinal microbiome.
Last Update: 2020-10-17
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. Introduction: Cardiovascular Disease (CVD) is a common disease that poses a serious threat to humans, with 15 million people dying from cardiovascular disease each year worldwide, the highest number of deaths. In addition to genetic and environmental factors, there has been increasing evidence in recent years that gut microbes have a profound impact on cardiovascular health. However, the causal relationship between the gut microbiome and the development of cardiovascular disease has not been confirmed.
A recent study by scientists at the University of Toledo in the United States has shown that deep learning from artificial intelligence will enable the diagnosis and screening of cardiovascular disease through the gut microbiome without the need for a series of specialized tests. The machine learning model developed by the study, which identifies people with cardiovascular disease with a high probability, can be used as an additional test to save time and reduce costs.
the study was led by Professor Bina Joe, dean of the Department of Science and Pharmacology at Toledo University, and published in the journal Hypertension, entitled Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease. For the first time, the study identified the characteristics of the gut microbiome as a group of patients with cardiovascular disease and applied these characteristics to in-depth ai AI learning to develop diagnostic screening methods for diseases based on the gut microbiome.
Bina Joe, one of the study's authors, said: "Even if we don't yet know all of these mechanisms, the link between gut microbiotics and diseases such as high blood pressure and heart failure is clear. Using artificial intelligence, our team developed a machine learning model that simply uses fecal bacterial characteristics to screen for cardiovascular disease. The
used computer algorithms to analyze the bacterial composition of stool samples from nearly 1,000 people. One group included 478 people with some form of cardiovascular disease, while the other group included 473 people who did not report any cardiovascular disease.
analysis found that certain bacteria were high in stool samples from each group of people. This essentially provides a microbial characteristic that shows a difference between the gut bacteria of people with cardiovascular disease and those of healthy people. As a result, the researchers identified a total of 39 different bacterial groups associated with cardiovascular health.
the researchers then applied these intestinal bacteria characteristics to a machine learning model that allowed the model to screen individuals for cardiovascular disease based on bacteria in stool samples.
"Even with significant technological advances in recent years, cardiovascular disease remains the leading cause of death worldwide," said Sachin Aryal of the UToledo School of Medicine and Life Sciences, the study's lead author. There are a number of diagnostic methods available to diagnose cardiovascular disease, but the overall assessment of cardiovascular health is still lagging behind. Our research shows that further development of intestinal bacterial-based methods to diagnose cardiovascular disease has broad prospects. "
machine learning models are highly likely to identify people with cardiovascular disease and can therefore be used by doctors as additional testing and treatment interventions, saving time and reducing costs."
. "I'm glad that our research has received so much attention, and I'm glad that one day these studies could serve as a basis for developing new approaches to the diagnosis of cardiovascular disease," Aryal said. It
worth noting that the 1,000 samples initially used by the researchers focused only on cardiovascular disease in general and not on specific diseases such as high blood pressure or heart failure. Artificial intelligence can produce more accurate results, including diagnosis in specific situations, by adding more nuanced data, such as individual situations or demographic information.
machine is very similar to a doctor, " says the researchers. If doctors see more patients, they can gain more experience and expertise and further improve their future diagnosis, which is the direction of our future research -- combining artificial intelligence and machine learning with traditional biomedical research. "
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