A new AI-driven method developed by Dutch researchers can now predict whether the antidepressant sertraline has an effect using brain scans and questionnaires. The technique has already made correct predictions in two-thirds of cases. This could shorten the treatment time to assess a drug’s effectiveness from months to just one week.
Why this is important:
It takes a long time to determine whether an antidepressant is working or not. And that’s annoying because people struggling with depression experience many unpleasant symptoms. Dutch research shows that AI can make the match between the patient and an antidepressant much faster.
Scientists from Amsterdam UMC and Radboudumc have developed a predictive model that can predict the effectiveness of sertraline by analyzing brain scans and questionnaires. The study, published in the American Journal of Psychiatry, highlights the role of brain perfusion as an indicator and has the potential to improve treatment protocols for depression dramatically. With 126,000 sertraline users annually in the Netherlands, this development could have a significant impact on mental health care.
Depression is one of the most common mental disorders in the Netherlands; one million people experience this condition. Finding the proper medication is a long and often daunting process.
The usual period for determining whether sertraline is effective can be as long as two months. Patients may unnecessarily suffer side effects with no noticeable benefit during this time. However, the new AI method can shorten this period drastically. The benefits speak for themselves: less exposure to unwanted side effects, faster relief of depressive symptoms, and more efficient use of health care resources.
Better precision in psychiatry
Liesbeth Reneman, lead researcher and professor of neuroradiology at Amsterdam UMC told Dutch newspaper NRC that psychiatrists currently have few concrete tools to assess which drug will be effective. After analyzing MRI images and questionnaires, the new algorithm correctly predicts whether sertraline will work in two-thirds of cases. This is a significant improvement over the current trial-and-error approach.
The method developed could revolutionize the way medication for depression is prescribed. By analyzing blood flow in parts of the brain where emotions are regulated, the algorithm can evaluate responsiveness to the drug. “If the blood flow is good, the medication can do its job better,” Reneman said.
Integration of clinical and imaging data
The study used data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a randomized clinical trial from the United States. The researchers followed 229 adult patients with recurrent or chronic depression. The AI-integrated MRI data and clinical assessments provided a comprehensive picture of the patient before and after one week of treatment with sertraline or a placebo.
The results of the study are promising. The model’s predictive performance for response to sertraline was significantly better than chance. Furthermore, multimodal models, which combine multiple types of data, were found to outperform unimodal models. This confirms that an integrated approach increases the precision of predictions.
Innovation in the treatment room
The new technique must still be fully in the starting blocks for clinical implementation. Psychiatrists must be able to refer patients for a brain scan, and the algorithm must be integrated into treatment protocols. Expectations are that when this is realized, the number of unnecessary prescriptions of sertraline will decrease significantly.
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