AI and brain scans may strongly improve depression treatment

In a new study, researchers found that artificial intelligence (AI) may soon play a critical role in choosing which depression therapy is best for patients.

They found a computer can accurately predict whether an antidepressant will work based on a patient’s brain activity.

The new research is the latest among several studies from the trial that cumulatively show how high-tech strategies can help doctors diagnose and prescribe depression treatments.

Although implementing these approaches will take time, researchers predict tools such as AI, brain imaging, and blood tests will revolutionize the field of psychiatry in the coming years.

The research was conducted by a team at UT Southwestern and elsewhere.

The study included more than 300 participants with depression who were randomly chosen to receive either a placebo or an SSRI (selective serotonin reuptake inhibitor), the most common class of antidepressants.

The researchers used an electroencephalogram, or EEG, to measure electrical activity in the participants’ cortex before they began treatment.

The team then developed a machine-learning algorithm (AI) to analyze and use the EEG data to predict which patients would benefit from the medication within two months.

Not only did the AI accurately predict outcomes, but further research also suggested that patients who were doubtful to respond to an antidepressant were likely to improve with other interventions such as psychotherapy or brain stimulation.

The findings were validated in three additional patient groups.

The team says the study shows that scientists can predict who benefits from an antidepressant, and actually brings it to the point of practical utility.

Among the next steps, researchers say, is developing an AI interface that can be widely integrated with EEGs across the country, as well as seeking approval from the U.S. Food and Drug Administration.

One author of the study is Madhukar Trivedi, M.D., a UT Southwestern psychiatrist.

The study is published in Nature Biotechnology.

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