The economic burden of mental health issues in the United States is staggering. Depression alone is estimated to cost $210 billion annually, with more than half of this deriving from absenteeism and reduced productivity in the workplace.
A major contributing factor to the struggle that is faced with how to treat depression is that depression is still not well understood in both research and clinical practice settings. Patients often also present with different symptoms which makes it difficult to diagnose.
When depression is diagnosed, it can take weeks, months or even longer of trying different medications to find something that might work. Patients usually are started on an antidepressant which takes about a month before results are seen. Studies however have shown that only 30% of patients respond to the first drug they are prescribed. In practice, doctors admit that it is trial and error when it comes to treatment selections for these individuals.
Where this situation may seem dire, promising new research has been published suggesting that artificial intelligence (AI) can help predict whether an antidepressant will work on a patient. A study was conducted where the brainwaves of individuals who had been diagnosed with major depression were analyzed. The researchers introduced an antidepressant to a random group of the participants and used an electroencephalogram (EEG) to measure electrical activity in the cortex. They subsequently developed a machine learning algorithm to analyze and use the EEG data to predict which patients would benefit from the antidepressant.
The outcome of the study found that 65% of the participants with a particular brainwave signature showed a positive response to the antidepressant. This is a remarkable outcome as it will take part of the guess factor out of prescribing antidepressants in certain individuals with depression.
Most psychiatrist and psychologist offices already have EEG equipment. As a result, it would only be a matter of collecting the brainwave activity and uploading the data. This test would be quick, is relatively cost effective and can provide a high clinical impact to these patients. These individuals will now have confidence in the treatment they are receiving.
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