AI could play ‘critical’ role in identifying appropriate treatment for depression
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A large-scale trial led by scientists at the University of Texas Southwestern (UT Southwestern) has produced a machine learning algorithm which accurately predicts the efficacy of an antidepressant, based on a patient’s neural activity.
The UT Southwestern researchers hope that this tool could eventually play a critical role in deciding which course of treatment would be best for patients with depression, as well as being part of a new generation of “biology-based, objective strategies” which make use of technologies such as AI to treat psychiatric disorders.
The US-wide trial was initiated in 2011 with the intention of better understanding mood disorders such as major depression and seasonal affective disorder (Sad). The trial has reaped many studies, the latest of which demonstrates that doctors could use computational tools to guide treatment choices for depression. The study was published in Nature Biotechnology.
“These studies have been a bigger success than anyone on our team could have imagined,” said Dr. Madhukar Trivedi, the UT Southwestern psychiatrist who oversaw the trial. “We provided abundant data to show we can move past the guessing game of choosing depression treatments and alter the mindset of how the disease should be diagnosed and treated.”
This 16-week trial involved more than 300 participants with depression, who either received a placebo or SSRI (selective serotonin reuptake inhibitor), the most common type of antidepressant. Despite the widespread prescription of SSRIs, they have been criticised for their side effects and for inefficacy in many patients.
Trivedi had previously established in another study that up to two-thirds of patients do not adequately respond to their first antidepressant, motivating him to find a way of identifying much earlier which treatment path is most likely to help the patient before they begin and potentially suffer further through ineffectual treatment.
Trivedi and his collaborators used an electroencephalogram (EEG) to measure electrical activity in the participants’ cortex before they began the treatment. This data was used to develop a machine learning algorithm to predict which patients would benefit from the medication within two months.
The researchers found that the AI accurately predicted outcomes, with patients less certain to respond to an antidepressant more likely to improve with other interventions, such as brain stimulation or therapeutic approaches. Their findings were replicated across three additional patient groups.
“It can be devastating for a patient when an antidepressant doesn’t work,” Trivedi said. “Our research is showing that they no longer have to endure the painful process of trial and error.”
Dr Amit Etkin, a Stanford University professor of psychiatry who also worked on the algorithm, added: “This study takes previous research, showing that we can predict who benefits from an antidepressant, and actually brings it to the point of practical utility.”
Next, they hope to develop an interface for the algorithm to be used alongside EEGs – and perhaps also with other means of measuring brain activity like functional magnetic resonance imaging (functional MRI, aka fMRI) or MEG – and have the system approved by the US Food and Drug Administration.
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