A study by the Mayo Clinic in the United States showed that the use of artificial intelligence technology for ECG analysis can accurately screen early asymptomatic left ventricular dysfunction indicators, which is better than other common screening methods.
Asymptomatic left ventricular dysfunction is a precursor to heart failure, affecting the lives of 7 million Americans. Although this heart disease can be treated after diagnosis, there is currently no cheap, non-invasive and painless screening tool for doctors to diagnose. Common diagnostic methods, such as echocardiography, computed tomography or magnetic resonance imaging scans, are expensive and not readily available. If you can develop cheap and fast diagnostic methods, it will be of great significance for the treatment of this disease.
In the new study, Mayo Clinic researchers have targeted the hotspot of current medical research – artificial intelligence technology applications. They believe that asymptomatic left ventricular dysfunction can be reliably detected in an electrocardiogram through a properly trained neural network. To validate, the researchers created a neural network and screened more than 600,000 pairs of matched ECGs and transthoracic echocardiograms from the clinic data to train, validate, and test the neural network. The results show that artificial intelligence applied to standard ECG analysis can reliably detect asymptomatic left ventricular dysfunction, and the accuracy is better than other common screening tests. Moreover, this screening method not only identifies asymptomatic diseases, but also predicts the risk of future illness. In patients without ventricular dysfunction, patients with positive AI screening were 4 times more likely to develop ventricular dysfunction in the future. The researchers believe that it is likely that artificial intelligence can identify very early, subtle ECG changes that occur before myocardial weakness and make judgments based on this.
The researchers pointed out that ECG is a very easy-to-access, low-cost detection method. It can be digitally processed by artificial intelligence, which can extract hidden new information of heart disease, which is simple and affordable. This is of great significance for the diagnosis and treatment of heart disease. .
The relevant research results were published in the journal Nature Medicine.
Some key features of ADA room number signs include:
1. Raised Characters: The room number must be raised or embossed to allow tactile reading by individuals with visual impairments.
2. Braille: ADA signs must include Grade 2 Braille, which is a system of raised dots that allows individuals with visual impairments to read through touch.
3. High Contrast: The color contrast between the background and the characters must be high to ensure readability for people with low vision.
4. Pictograms: In addition to the room number, ADA signs may include pictograms or symbols to provide additional information, such as indicating accessible restrooms or wheelchair accessibility.
5. Mounting Height: ADA guidelines specify the appropriate mounting height for room number signs to ensure they are easily visible and accessible for individuals using wheelchairs or mobility aids.
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