High blood pressure is one of the most common and dangerous health problems in the world. It is often called a “silent killer” because many people do not feel any symptoms until serious damage has already occurred.
According to global estimates, high blood pressure contributes to around 10 million deaths every year. It can lead to heart attacks, strokes, kidney failure, and other life-threatening conditions.
Most people think that checking blood pressure at the doctor’s office is enough to know whether they have hypertension.
However, this is not always true. About 10% of people with high blood pressure actually show normal readings during a medical visit. This condition is known as masked hypertension. It is dangerous because it can go unnoticed and untreated for years.
To detect masked hypertension, doctors usually rely on a device called an ambulatory blood pressure monitor. This machine records blood pressure over 24 hours while a person goes about their daily life.
Although it is effective, it is not always practical. The device can be uncomfortable to wear, expensive, and not widely available, especially in low-income regions. Because of this, many patients with masked hypertension are never diagnosed.
Now, researchers at the University of Arkansas have developed a new tool that could change this. Their study, published in the journal Frontiers in Physiology, describes an artificial intelligence system designed to detect masked hypertension using routine health data.
The researchers trained their system using data from the African-PREDICT study. This project collected detailed health information from about 1,200 young adults in South Africa.
The participants were in their 20s and 30s and came from different backgrounds. Importantly, all of them wore ambulatory monitors, so researchers knew exactly who had masked hypertension and who did not.
The AI system studied this information and looked for patterns. It compared people with masked hypertension to those with normal blood pressure and identified similarities within each group. By doing this, it learned how to predict the condition based on factors such as age, sex, body measurements, and substances found in the blood.
When tested, the system was able to correctly identify masked hypertension about 83% of the time. It also had a low rate of false positives, meaning it was less likely to wrongly label someone as having the condition.
This approach is similar to how doctors use cholesterol levels to estimate heart disease risk. However, instead of using just a few measurements, this model looks at many different pieces of information at once. This allows it to find connections that humans might miss.
The tool could eventually be added to medical record systems. Doctors would not need special equipment. Instead, the system would analyze existing patient data and flag those at risk. Over time, as more data is added, the system could become even more accurate.
However, this study has some limits. The data mainly came from young adults in one country, which means the results may not apply to older people or other populations. More studies are needed to confirm the findings and improve the model.
Overall, the research shows that artificial intelligence could play an important role in improving healthcare. It may help doctors find hidden conditions earlier and provide treatment sooner. This could save lives and reduce the burden of disease worldwide.
If you care about high blood pressure, please read studies that early time-restricted eating could help improve blood pressure, and natural coconut sugar could help reduce blood pressure and artery stiffness.
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Source: University of Arkansas.
